{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1c50055d",
   "metadata": {},
   "source": [
    "## ИМПОРТ БИБИЛИОТЕК"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d267ae15",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import missingno as msno\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "import numpy as np\n",
    "sns.set()\n",
    "\n",
    "# выключаем вывод ошибок для более чистого вида ноутбука\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92309f61",
   "metadata": {},
   "source": [
    "## ЗАГРУЗКА ДАННЫХ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0deb699e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>session_id</th>\n",
       "      <th>client_id</th>\n",
       "      <th>visit_date</th>\n",
       "      <th>visit_time</th>\n",
       "      <th>visit_number</th>\n",
       "      <th>utm_source</th>\n",
       "      <th>utm_medium</th>\n",
       "      <th>utm_campaign</th>\n",
       "      <th>utm_adcontent</th>\n",
       "      <th>utm_keyword</th>\n",
       "      <th>device_category</th>\n",
       "      <th>device_os</th>\n",
       "      <th>device_brand</th>\n",
       "      <th>device_model</th>\n",
       "      <th>device_screen_resolution</th>\n",
       "      <th>device_browser</th>\n",
       "      <th>geo_country</th>\n",
       "      <th>geo_city</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9055434745589932991.1637753792.1637753792</td>\n",
       "      <td>2108382700.163776</td>\n",
       "      <td>2021-11-24</td>\n",
       "      <td>14:36:32</td>\n",
       "      <td>1</td>\n",
       "      <td>ZpYIoDJMcFzVoPFsHGJL</td>\n",
       "      <td>banner</td>\n",
       "      <td>LEoPHuyFvzoNfnzGgfcd</td>\n",
       "      <td>vCIpmpaGBnIQhyYNkXqp</td>\n",
       "      <td>puhZPIYqKXeFPaUviSjo</td>\n",
       "      <td>mobile</td>\n",
       "      <td>Android</td>\n",
       "      <td>Huawei</td>\n",
       "      <td>NaN</td>\n",
       "      <td>360x720</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Zlatoust</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>905544597018549464.1636867290.1636867290</td>\n",
       "      <td>210838531.163687</td>\n",
       "      <td>2021-11-14</td>\n",
       "      <td>08:21:30</td>\n",
       "      <td>1</td>\n",
       "      <td>MvfHsxITijuriZxsqZqt</td>\n",
       "      <td>cpm</td>\n",
       "      <td>FTjNLDyTrXaWYgZymFkV</td>\n",
       "      <td>xhoenQgDQsgfEPYNPwKO</td>\n",
       "      <td>IGUCNvHlhfHpROGclCit</td>\n",
       "      <td>mobile</td>\n",
       "      <td>Android</td>\n",
       "      <td>Samsung</td>\n",
       "      <td>NaN</td>\n",
       "      <td>385x854</td>\n",
       "      <td>Samsung Internet</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Moscow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9055446045651783499.1640648526.1640648526</td>\n",
       "      <td>2108385331.164065</td>\n",
       "      <td>2021-12-28</td>\n",
       "      <td>02:42:06</td>\n",
       "      <td>1</td>\n",
       "      <td>ZpYIoDJMcFzVoPFsHGJL</td>\n",
       "      <td>banner</td>\n",
       "      <td>LEoPHuyFvzoNfnzGgfcd</td>\n",
       "      <td>vCIpmpaGBnIQhyYNkXqp</td>\n",
       "      <td>puhZPIYqKXeFPaUviSjo</td>\n",
       "      <td>mobile</td>\n",
       "      <td>Android</td>\n",
       "      <td>Huawei</td>\n",
       "      <td>NaN</td>\n",
       "      <td>360x720</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Krasnoyarsk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9055447046360770272.1622255328.1622255328</td>\n",
       "      <td>2108385564.162225</td>\n",
       "      <td>2021-05-29</td>\n",
       "      <td>05:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>kjsLglQLzykiRbcDiGcD</td>\n",
       "      <td>cpc</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NOBKLgtuvqYWkXQHeYWM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mobile</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Xiaomi</td>\n",
       "      <td>NaN</td>\n",
       "      <td>393x786</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Moscow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9055447046360770272.1622255345.1622255345</td>\n",
       "      <td>2108385564.162225</td>\n",
       "      <td>2021-05-29</td>\n",
       "      <td>05:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>kjsLglQLzykiRbcDiGcD</td>\n",
       "      <td>cpc</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mobile</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Xiaomi</td>\n",
       "      <td>NaN</td>\n",
       "      <td>393x786</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Moscow</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  session_id          client_id  visit_date  \\\n",
       "0  9055434745589932991.1637753792.1637753792  2108382700.163776  2021-11-24   \n",
       "1   905544597018549464.1636867290.1636867290   210838531.163687  2021-11-14   \n",
       "2  9055446045651783499.1640648526.1640648526  2108385331.164065  2021-12-28   \n",
       "3  9055447046360770272.1622255328.1622255328  2108385564.162225  2021-05-29   \n",
       "4  9055447046360770272.1622255345.1622255345  2108385564.162225  2021-05-29   \n",
       "\n",
       "  visit_time  visit_number            utm_source utm_medium  \\\n",
       "0   14:36:32             1  ZpYIoDJMcFzVoPFsHGJL     banner   \n",
       "1   08:21:30             1  MvfHsxITijuriZxsqZqt        cpm   \n",
       "2   02:42:06             1  ZpYIoDJMcFzVoPFsHGJL     banner   \n",
       "3   05:00:00             1  kjsLglQLzykiRbcDiGcD        cpc   \n",
       "4   05:00:00             2  kjsLglQLzykiRbcDiGcD        cpc   \n",
       "\n",
       "           utm_campaign         utm_adcontent           utm_keyword  \\\n",
       "0  LEoPHuyFvzoNfnzGgfcd  vCIpmpaGBnIQhyYNkXqp  puhZPIYqKXeFPaUviSjo   \n",
       "1  FTjNLDyTrXaWYgZymFkV  xhoenQgDQsgfEPYNPwKO  IGUCNvHlhfHpROGclCit   \n",
       "2  LEoPHuyFvzoNfnzGgfcd  vCIpmpaGBnIQhyYNkXqp  puhZPIYqKXeFPaUviSjo   \n",
       "3                   NaN  NOBKLgtuvqYWkXQHeYWM                   NaN   \n",
       "4                   NaN                   NaN                   NaN   \n",
       "\n",
       "  device_category device_os device_brand device_model  \\\n",
       "0          mobile   Android       Huawei          NaN   \n",
       "1          mobile   Android      Samsung          NaN   \n",
       "2          mobile   Android       Huawei          NaN   \n",
       "3          mobile       NaN       Xiaomi          NaN   \n",
       "4          mobile       NaN       Xiaomi          NaN   \n",
       "\n",
       "  device_screen_resolution    device_browser geo_country     geo_city  \n",
       "0                  360x720            Chrome      Russia     Zlatoust  \n",
       "1                  385x854  Samsung Internet      Russia       Moscow  \n",
       "2                  360x720            Chrome      Russia  Krasnoyarsk  \n",
       "3                  393x786            Chrome      Russia       Moscow  \n",
       "4                  393x786            Chrome      Russia       Moscow  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sessions = pd.read_csv('ga_sessions.csv')\n",
    "df_sessions.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e1b49dfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>session_id</th>\n",
       "      <th>hit_date</th>\n",
       "      <th>hit_time</th>\n",
       "      <th>hit_number</th>\n",
       "      <th>hit_type</th>\n",
       "      <th>hit_referer</th>\n",
       "      <th>hit_page_path</th>\n",
       "      <th>event_category</th>\n",
       "      <th>event_action</th>\n",
       "      <th>event_label</th>\n",
       "      <th>event_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5639623078712724064.1640254056.1640254056</td>\n",
       "      <td>2021-12-23</td>\n",
       "      <td>597864.0</td>\n",
       "      <td>30</td>\n",
       "      <td>event</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sberauto.com/cars?utm_source_initial=google&amp;ut...</td>\n",
       "      <td>quiz</td>\n",
       "      <td>quiz_show</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7750352294969115059.1640271109.1640271109</td>\n",
       "      <td>2021-12-23</td>\n",
       "      <td>597331.0</td>\n",
       "      <td>41</td>\n",
       "      <td>event</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sberauto.com/cars/fiat?city=1&amp;city=18&amp;rental_c...</td>\n",
       "      <td>quiz</td>\n",
       "      <td>quiz_show</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>885342191847998240.1640235807.1640235807</td>\n",
       "      <td>2021-12-23</td>\n",
       "      <td>796252.0</td>\n",
       "      <td>49</td>\n",
       "      <td>event</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sberauto.com/cars/all/volkswagen/polo/e994838f...</td>\n",
       "      <td>quiz</td>\n",
       "      <td>quiz_show</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>142526202120934167.1640211014.1640211014</td>\n",
       "      <td>2021-12-23</td>\n",
       "      <td>934292.0</td>\n",
       "      <td>46</td>\n",
       "      <td>event</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sberauto.com/cars?utm_source_initial=yandex&amp;ut...</td>\n",
       "      <td>quiz</td>\n",
       "      <td>quiz_show</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3450086108837475701.1640265078.1640265078</td>\n",
       "      <td>2021-12-23</td>\n",
       "      <td>768741.0</td>\n",
       "      <td>79</td>\n",
       "      <td>event</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sberauto.com/cars/all/mercedes-benz/cla-klasse...</td>\n",
       "      <td>quiz</td>\n",
       "      <td>quiz_show</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  session_id    hit_date  hit_time  \\\n",
       "0  5639623078712724064.1640254056.1640254056  2021-12-23  597864.0   \n",
       "1  7750352294969115059.1640271109.1640271109  2021-12-23  597331.0   \n",
       "2   885342191847998240.1640235807.1640235807  2021-12-23  796252.0   \n",
       "3   142526202120934167.1640211014.1640211014  2021-12-23  934292.0   \n",
       "4  3450086108837475701.1640265078.1640265078  2021-12-23  768741.0   \n",
       "\n",
       "   hit_number hit_type hit_referer  \\\n",
       "0          30    event         NaN   \n",
       "1          41    event         NaN   \n",
       "2          49    event         NaN   \n",
       "3          46    event         NaN   \n",
       "4          79    event         NaN   \n",
       "\n",
       "                                       hit_page_path event_category  \\\n",
       "0  sberauto.com/cars?utm_source_initial=google&ut...           quiz   \n",
       "1  sberauto.com/cars/fiat?city=1&city=18&rental_c...           quiz   \n",
       "2  sberauto.com/cars/all/volkswagen/polo/e994838f...           quiz   \n",
       "3  sberauto.com/cars?utm_source_initial=yandex&ut...           quiz   \n",
       "4  sberauto.com/cars/all/mercedes-benz/cla-klasse...           quiz   \n",
       "\n",
       "  event_action event_label  event_value  \n",
       "0    quiz_show         NaN          NaN  \n",
       "1    quiz_show         NaN          NaN  \n",
       "2    quiz_show         NaN          NaN  \n",
       "3    quiz_show         NaN          NaN  \n",
       "4    quiz_show         NaN          NaN  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hits = pd.read_csv('ga_hits-002.csv')\n",
    "df_hits.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e60206cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "session_csv: (1860042, 18)\n",
      "hits_csv: (15726470, 11)\n"
     ]
    }
   ],
   "source": [
    "print('session_csv:', df_sessions.shape)\n",
    "print('hits_csv:', df_hits.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6ad435b",
   "metadata": {},
   "source": [
    "## Data Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2cf4898",
   "metadata": {},
   "source": [
    "### Проверка дубликатов"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca40b785",
   "metadata": {},
   "source": [
    "Для начала мы проверим дубликаты в df_sessions и в df_hits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "357d6f82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sessions.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dd75054c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hits.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff512a4d",
   "metadata": {},
   "source": [
    "### Проверка и преобразование типов данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "26e2071d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "session_id                  object\n",
       "client_id                   object\n",
       "visit_date                  object\n",
       "visit_time                  object\n",
       "visit_number                 int64\n",
       "utm_source                  object\n",
       "utm_medium                  object\n",
       "utm_campaign                object\n",
       "utm_adcontent               object\n",
       "utm_keyword                 object\n",
       "device_category             object\n",
       "device_os                   object\n",
       "device_brand                object\n",
       "device_model                object\n",
       "device_screen_resolution    object\n",
       "device_browser              object\n",
       "geo_country                 object\n",
       "geo_city                    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sessions.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fada003e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "session_id         object\n",
       "hit_date           object\n",
       "hit_time          float64\n",
       "hit_number          int64\n",
       "hit_type           object\n",
       "hit_referer        object\n",
       "hit_page_path      object\n",
       "event_category     object\n",
       "event_action       object\n",
       "event_label        object\n",
       "event_value       float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hits.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e9f8f943",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>session_id</th>\n",
       "      <th>client_id</th>\n",
       "      <th>visit_date</th>\n",
       "      <th>visit_time</th>\n",
       "      <th>visit_number</th>\n",
       "      <th>utm_source</th>\n",
       "      <th>utm_medium</th>\n",
       "      <th>utm_campaign</th>\n",
       "      <th>utm_adcontent</th>\n",
       "      <th>utm_keyword</th>\n",
       "      <th>device_category</th>\n",
       "      <th>device_os</th>\n",
       "      <th>device_brand</th>\n",
       "      <th>device_model</th>\n",
       "      <th>device_screen_resolution</th>\n",
       "      <th>device_browser</th>\n",
       "      <th>geo_country</th>\n",
       "      <th>geo_city</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1860042</td>\n",
       "      <td>1.860042e+06</td>\n",
       "      <td>1860042</td>\n",
       "      <td>1860042</td>\n",
       "      <td>1.860042e+06</td>\n",
       "      <td>1859945</td>\n",
       "      <td>1860042</td>\n",
       "      <td>1640439</td>\n",
       "      <td>1524427</td>\n",
       "      <td>777981</td>\n",
       "      <td>1860042</td>\n",
       "      <td>789904</td>\n",
       "      <td>1492864</td>\n",
       "      <td>16338</td>\n",
       "      <td>1860042</td>\n",
       "      <td>1860042</td>\n",
       "      <td>1860042</td>\n",
       "      <td>1860042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>1860042</td>\n",
       "      <td>1.391717e+06</td>\n",
       "      <td>226</td>\n",
       "      <td>85318</td>\n",
       "      <td>NaN</td>\n",
       "      <td>293</td>\n",
       "      <td>56</td>\n",
       "      <td>412</td>\n",
       "      <td>286</td>\n",
       "      <td>1219</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>205</td>\n",
       "      <td>104</td>\n",
       "      <td>5039</td>\n",
       "      <td>57</td>\n",
       "      <td>166</td>\n",
       "      <td>2548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>9055434745589932991.1637753792.1637753792</td>\n",
       "      <td>1.750498e+09</td>\n",
       "      <td>2021-05-24</td>\n",
       "      <td>12:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ZpYIoDJMcFzVoPFsHGJL</td>\n",
       "      <td>banner</td>\n",
       "      <td>LTuZkdKfxRGVceoWkVyg</td>\n",
       "      <td>JNHcPlZPxEMWDnRiyoBf</td>\n",
       "      <td>puhZPIYqKXeFPaUviSjo</td>\n",
       "      <td>mobile</td>\n",
       "      <td>Android</td>\n",
       "      <td>Apple</td>\n",
       "      <td>AuMdmADEIoPXiWpTsBEj</td>\n",
       "      <td>414x896</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Moscow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>4.620000e+02</td>\n",
       "      <td>39453</td>\n",
       "      <td>61067</td>\n",
       "      <td>NaN</td>\n",
       "      <td>578290</td>\n",
       "      <td>552272</td>\n",
       "      <td>463481</td>\n",
       "      <td>1006599</td>\n",
       "      <td>506819</td>\n",
       "      <td>1474871</td>\n",
       "      <td>464054</td>\n",
       "      <td>551088</td>\n",
       "      <td>9778</td>\n",
       "      <td>169090</td>\n",
       "      <td>1013436</td>\n",
       "      <td>1800565</td>\n",
       "      <td>805329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.712804e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.182907e+01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.640000e+02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       session_id     client_id  visit_date  \\\n",
       "count                                     1860042  1.860042e+06     1860042   \n",
       "unique                                    1860042  1.391717e+06         226   \n",
       "top     9055434745589932991.1637753792.1637753792  1.750498e+09  2021-05-24   \n",
       "freq                                            1  4.620000e+02       39453   \n",
       "mean                                          NaN           NaN         NaN   \n",
       "std                                           NaN           NaN         NaN   \n",
       "min                                           NaN           NaN         NaN   \n",
       "25%                                           NaN           NaN         NaN   \n",
       "50%                                           NaN           NaN         NaN   \n",
       "75%                                           NaN           NaN         NaN   \n",
       "max                                           NaN           NaN         NaN   \n",
       "\n",
       "       visit_time  visit_number            utm_source utm_medium  \\\n",
       "count     1860042  1.860042e+06               1859945    1860042   \n",
       "unique      85318           NaN                   293         56   \n",
       "top      12:00:00           NaN  ZpYIoDJMcFzVoPFsHGJL     banner   \n",
       "freq        61067           NaN                578290     552272   \n",
       "mean          NaN  2.712804e+00                   NaN        NaN   \n",
       "std           NaN  1.182907e+01                   NaN        NaN   \n",
       "min           NaN  1.000000e+00                   NaN        NaN   \n",
       "25%           NaN  1.000000e+00                   NaN        NaN   \n",
       "50%           NaN  1.000000e+00                   NaN        NaN   \n",
       "75%           NaN  2.000000e+00                   NaN        NaN   \n",
       "max           NaN  5.640000e+02                   NaN        NaN   \n",
       "\n",
       "                utm_campaign         utm_adcontent           utm_keyword  \\\n",
       "count                1640439               1524427                777981   \n",
       "unique                   412                   286                  1219   \n",
       "top     LTuZkdKfxRGVceoWkVyg  JNHcPlZPxEMWDnRiyoBf  puhZPIYqKXeFPaUviSjo   \n",
       "freq                  463481               1006599                506819   \n",
       "mean                     NaN                   NaN                   NaN   \n",
       "std                      NaN                   NaN                   NaN   \n",
       "min                      NaN                   NaN                   NaN   \n",
       "25%                      NaN                   NaN                   NaN   \n",
       "50%                      NaN                   NaN                   NaN   \n",
       "75%                      NaN                   NaN                   NaN   \n",
       "max                      NaN                   NaN                   NaN   \n",
       "\n",
       "       device_category device_os device_brand          device_model  \\\n",
       "count          1860042    789904      1492864                 16338   \n",
       "unique               3        13          205                   104   \n",
       "top             mobile   Android        Apple  AuMdmADEIoPXiWpTsBEj   \n",
       "freq           1474871    464054       551088                  9778   \n",
       "mean               NaN       NaN          NaN                   NaN   \n",
       "std                NaN       NaN          NaN                   NaN   \n",
       "min                NaN       NaN          NaN                   NaN   \n",
       "25%                NaN       NaN          NaN                   NaN   \n",
       "50%                NaN       NaN          NaN                   NaN   \n",
       "75%                NaN       NaN          NaN                   NaN   \n",
       "max                NaN       NaN          NaN                   NaN   \n",
       "\n",
       "       device_screen_resolution device_browser geo_country geo_city  \n",
       "count                   1860042        1860042     1860042  1860042  \n",
       "unique                     5039             57         166     2548  \n",
       "top                     414x896         Chrome      Russia   Moscow  \n",
       "freq                     169090        1013436     1800565   805329  \n",
       "mean                        NaN            NaN         NaN      NaN  \n",
       "std                         NaN            NaN         NaN      NaN  \n",
       "min                         NaN            NaN         NaN      NaN  \n",
       "25%                         NaN            NaN         NaN      NaN  \n",
       "50%                         NaN            NaN         NaN      NaN  \n",
       "75%                         NaN            NaN         NaN      NaN  \n",
       "max                         NaN            NaN         NaN      NaN  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sessions.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "940aa20e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>session_id</th>\n",
       "      <th>hit_date</th>\n",
       "      <th>hit_time</th>\n",
       "      <th>hit_number</th>\n",
       "      <th>hit_type</th>\n",
       "      <th>hit_referer</th>\n",
       "      <th>hit_page_path</th>\n",
       "      <th>event_category</th>\n",
       "      <th>event_action</th>\n",
       "      <th>event_label</th>\n",
       "      <th>event_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>15726470</td>\n",
       "      <td>15726470</td>\n",
       "      <td>6.566148e+06</td>\n",
       "      <td>1.572647e+07</td>\n",
       "      <td>15726470</td>\n",
       "      <td>9451666</td>\n",
       "      <td>15726470</td>\n",
       "      <td>15726470</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>1734610</td>\n",
       "      <td>226</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>37873</td>\n",
       "      <td>342715</td>\n",
       "      <td>52</td>\n",
       "      <td>230</td>\n",
       "      <td>39825</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>5442565791571325612.1632449195.1632449195</td>\n",
       "      <td>2021-05-24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>event</td>\n",
       "      <td>HbolMJUevblAbkHClEQa</td>\n",
       "      <td>podpiska.sberauto.com/</td>\n",
       "      <td>card_web</td>\n",
       "      <td>view_card</td>\n",
       "      <td>KclpemfoHstknWHFiLit</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>768</td>\n",
       "      <td>513035</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15726470</td>\n",
       "      <td>8879187</td>\n",
       "      <td>2793639</td>\n",
       "      <td>7456998</td>\n",
       "      <td>3558985</td>\n",
       "      <td>6505447</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.091050e+05</td>\n",
       "      <td>2.356715e+01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.032110e+05</td>\n",
       "      <td>2.887713e+01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.412000e+04</td>\n",
       "      <td>7.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.994100e+04</td>\n",
       "      <td>1.500000e+01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.195352e+05</td>\n",
       "      <td>2.900000e+01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.315688e+07</td>\n",
       "      <td>5.000000e+02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       session_id    hit_date      hit_time  \\\n",
       "count                                    15726470    15726470  6.566148e+06   \n",
       "unique                                    1734610         226           NaN   \n",
       "top     5442565791571325612.1632449195.1632449195  2021-05-24           NaN   \n",
       "freq                                          768      513035           NaN   \n",
       "mean                                          NaN         NaN  2.091050e+05   \n",
       "std                                           NaN         NaN  4.032110e+05   \n",
       "min                                           NaN         NaN  0.000000e+00   \n",
       "25%                                           NaN         NaN  2.412000e+04   \n",
       "50%                                           NaN         NaN  8.994100e+04   \n",
       "75%                                           NaN         NaN  2.195352e+05   \n",
       "max                                           NaN         NaN  1.315688e+07   \n",
       "\n",
       "          hit_number  hit_type           hit_referer           hit_page_path  \\\n",
       "count   1.572647e+07  15726470               9451666                15726470   \n",
       "unique           NaN         1                 37873                  342715   \n",
       "top              NaN     event  HbolMJUevblAbkHClEQa  podpiska.sberauto.com/   \n",
       "freq             NaN  15726470               8879187                 2793639   \n",
       "mean    2.356715e+01       NaN                   NaN                     NaN   \n",
       "std     2.887713e+01       NaN                   NaN                     NaN   \n",
       "min     1.000000e+00       NaN                   NaN                     NaN   \n",
       "25%     7.000000e+00       NaN                   NaN                     NaN   \n",
       "50%     1.500000e+01       NaN                   NaN                     NaN   \n",
       "75%     2.900000e+01       NaN                   NaN                     NaN   \n",
       "max     5.000000e+02       NaN                   NaN                     NaN   \n",
       "\n",
       "       event_category event_action           event_label  event_value  \n",
       "count        15726470     15726470              11966286          0.0  \n",
       "unique             52          230                 39825          NaN  \n",
       "top          card_web    view_card  KclpemfoHstknWHFiLit          NaN  \n",
       "freq          7456998      3558985               6505447          NaN  \n",
       "mean              NaN          NaN                   NaN          NaN  \n",
       "std               NaN          NaN                   NaN          NaN  \n",
       "min               NaN          NaN                   NaN          NaN  \n",
       "25%               NaN          NaN                   NaN          NaN  \n",
       "50%               NaN          NaN                   NaN          NaN  \n",
       "75%               NaN          NaN                   NaN          NaN  \n",
       "max               NaN          NaN                   NaN          NaN  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hits.describe(include='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42df6347",
   "metadata": {},
   "source": [
    "На данном этапе мы меняем тип данных на datetime в датасете df_sessions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d007508e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>session_id</th>\n",
       "      <th>client_id</th>\n",
       "      <th>visit_date</th>\n",
       "      <th>visit_time</th>\n",
       "      <th>visit_number</th>\n",
       "      <th>utm_source</th>\n",
       "      <th>utm_medium</th>\n",
       "      <th>utm_campaign</th>\n",
       "      <th>utm_adcontent</th>\n",
       "      <th>utm_keyword</th>\n",
       "      <th>device_category</th>\n",
       "      <th>device_os</th>\n",
       "      <th>device_brand</th>\n",
       "      <th>device_model</th>\n",
       "      <th>device_screen_resolution</th>\n",
       "      <th>device_browser</th>\n",
       "      <th>geo_country</th>\n",
       "      <th>geo_city</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1860042</td>\n",
       "      <td>1.860042e+06</td>\n",
       "      <td>1860042</td>\n",
       "      <td>1860042</td>\n",
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       "      <td>1859945</td>\n",
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       "      <td>1524427</td>\n",
       "      <td>777981</td>\n",
       "      <td>1860042</td>\n",
       "      <td>789904</td>\n",
       "      <td>1492864</td>\n",
       "      <td>16338</td>\n",
       "      <td>1860042</td>\n",
       "      <td>1860042</td>\n",
       "      <td>1860042</td>\n",
       "      <td>1860042</td>\n",
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       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>1860042</td>\n",
       "      <td>1.391717e+06</td>\n",
       "      <td>226</td>\n",
       "      <td>85318</td>\n",
       "      <td>NaN</td>\n",
       "      <td>293</td>\n",
       "      <td>56</td>\n",
       "      <td>412</td>\n",
       "      <td>286</td>\n",
       "      <td>1219</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>205</td>\n",
       "      <td>104</td>\n",
       "      <td>5039</td>\n",
       "      <td>57</td>\n",
       "      <td>166</td>\n",
       "      <td>2548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>9055434745589932991.1637753792.1637753792</td>\n",
       "      <td>1.750498e+09</td>\n",
       "      <td>2021-05-24 00:00:00</td>\n",
       "      <td>12:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ZpYIoDJMcFzVoPFsHGJL</td>\n",
       "      <td>banner</td>\n",
       "      <td>LTuZkdKfxRGVceoWkVyg</td>\n",
       "      <td>JNHcPlZPxEMWDnRiyoBf</td>\n",
       "      <td>puhZPIYqKXeFPaUviSjo</td>\n",
       "      <td>mobile</td>\n",
       "      <td>Android</td>\n",
       "      <td>Apple</td>\n",
       "      <td>AuMdmADEIoPXiWpTsBEj</td>\n",
       "      <td>414x896</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Moscow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>4.620000e+02</td>\n",
       "      <td>39453</td>\n",
       "      <td>61067</td>\n",
       "      <td>NaN</td>\n",
       "      <td>578290</td>\n",
       "      <td>552272</td>\n",
       "      <td>463481</td>\n",
       "      <td>1006599</td>\n",
       "      <td>506819</td>\n",
       "      <td>1474871</td>\n",
       "      <td>464054</td>\n",
       "      <td>551088</td>\n",
       "      <td>9778</td>\n",
       "      <td>169090</td>\n",
       "      <td>1013436</td>\n",
       "      <td>1800565</td>\n",
       "      <td>805329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-05-19 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-12-31 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>2.712804e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>1.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.640000e+02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       session_id     client_id  \\\n",
       "count                                     1860042  1.860042e+06   \n",
       "unique                                    1860042  1.391717e+06   \n",
       "top     9055434745589932991.1637753792.1637753792  1.750498e+09   \n",
       "freq                                            1  4.620000e+02   \n",
       "first                                         NaN           NaN   \n",
       "last                                          NaN           NaN   \n",
       "mean                                          NaN           NaN   \n",
       "std                                           NaN           NaN   \n",
       "min                                           NaN           NaN   \n",
       "25%                                           NaN           NaN   \n",
       "50%                                           NaN           NaN   \n",
       "75%                                           NaN           NaN   \n",
       "max                                           NaN           NaN   \n",
       "\n",
       "                 visit_date visit_time  visit_number            utm_source  \\\n",
       "count               1860042    1860042  1.860042e+06               1859945   \n",
       "unique                  226      85318           NaN                   293   \n",
       "top     2021-05-24 00:00:00   12:00:00           NaN  ZpYIoDJMcFzVoPFsHGJL   \n",
       "freq                  39453      61067           NaN                578290   \n",
       "first   2021-05-19 00:00:00        NaN           NaN                   NaN   \n",
       "last    2021-12-31 00:00:00        NaN           NaN                   NaN   \n",
       "mean                    NaN        NaN  2.712804e+00                   NaN   \n",
       "std                     NaN        NaN  1.182907e+01                   NaN   \n",
       "min                     NaN        NaN  1.000000e+00                   NaN   \n",
       "25%                     NaN        NaN  1.000000e+00                   NaN   \n",
       "50%                     NaN        NaN  1.000000e+00                   NaN   \n",
       "75%                     NaN        NaN  2.000000e+00                   NaN   \n",
       "max                     NaN        NaN  5.640000e+02                   NaN   \n",
       "\n",
       "       utm_medium          utm_campaign         utm_adcontent  \\\n",
       "count     1860042               1640439               1524427   \n",
       "unique         56                   412                   286   \n",
       "top        banner  LTuZkdKfxRGVceoWkVyg  JNHcPlZPxEMWDnRiyoBf   \n",
       "freq       552272                463481               1006599   \n",
       "first         NaN                   NaN                   NaN   \n",
       "last          NaN                   NaN                   NaN   \n",
       "mean          NaN                   NaN                   NaN   \n",
       "std           NaN                   NaN                   NaN   \n",
       "min           NaN                   NaN                   NaN   \n",
       "25%           NaN                   NaN                   NaN   \n",
       "50%           NaN                   NaN                   NaN   \n",
       "75%           NaN                   NaN                   NaN   \n",
       "max           NaN                   NaN                   NaN   \n",
       "\n",
       "                 utm_keyword device_category device_os device_brand  \\\n",
       "count                 777981         1860042    789904      1492864   \n",
       "unique                  1219               3        13          205   \n",
       "top     puhZPIYqKXeFPaUviSjo          mobile   Android        Apple   \n",
       "freq                  506819         1474871    464054       551088   \n",
       "first                    NaN             NaN       NaN          NaN   \n",
       "last                     NaN             NaN       NaN          NaN   \n",
       "mean                     NaN             NaN       NaN          NaN   \n",
       "std                      NaN             NaN       NaN          NaN   \n",
       "min                      NaN             NaN       NaN          NaN   \n",
       "25%                      NaN             NaN       NaN          NaN   \n",
       "50%                      NaN             NaN       NaN          NaN   \n",
       "75%                      NaN             NaN       NaN          NaN   \n",
       "max                      NaN             NaN       NaN          NaN   \n",
       "\n",
       "                device_model device_screen_resolution device_browser  \\\n",
       "count                  16338                  1860042        1860042   \n",
       "unique                   104                     5039             57   \n",
       "top     AuMdmADEIoPXiWpTsBEj                  414x896         Chrome   \n",
       "freq                    9778                   169090        1013436   \n",
       "first                    NaN                      NaN            NaN   \n",
       "last                     NaN                      NaN            NaN   \n",
       "mean                     NaN                      NaN            NaN   \n",
       "std                      NaN                      NaN            NaN   \n",
       "min                      NaN                      NaN            NaN   \n",
       "25%                      NaN                      NaN            NaN   \n",
       "50%                      NaN                      NaN            NaN   \n",
       "75%                      NaN                      NaN            NaN   \n",
       "max                      NaN                      NaN            NaN   \n",
       "\n",
       "       geo_country geo_city  \n",
       "count      1860042  1860042  \n",
       "unique         166     2548  \n",
       "top         Russia   Moscow  \n",
       "freq       1800565   805329  \n",
       "first          NaN      NaN  \n",
       "last           NaN      NaN  \n",
       "mean           NaN      NaN  \n",
       "std            NaN      NaN  \n",
       "min            NaN      NaN  \n",
       "25%            NaN      NaN  \n",
       "50%            NaN      NaN  \n",
       "75%            NaN      NaN  \n",
       "max            NaN      NaN  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sessions.visit_date = pd.to_datetime(df_sessions.visit_date)\n",
    "df_sessions.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e68d9cbd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "session_id                          object\n",
       "client_id                           object\n",
       "visit_date                  datetime64[ns]\n",
       "visit_time                          object\n",
       "visit_number                         int64\n",
       "utm_source                          object\n",
       "utm_medium                          object\n",
       "utm_campaign                        object\n",
       "utm_adcontent                       object\n",
       "utm_keyword                         object\n",
       "device_category                     object\n",
       "device_os                           object\n",
       "device_brand                        object\n",
       "device_model                        object\n",
       "device_screen_resolution            object\n",
       "device_browser                      object\n",
       "geo_country                         object\n",
       "geo_city                            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sessions.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4cbea47",
   "metadata": {},
   "source": [
    "Здесь мы меняем тип данных на datetime во втором датасете (df_hits) и убираем столбец hit_time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3de5f71e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>session_id</th>\n",
       "      <th>hit_date</th>\n",
       "      <th>hit_number</th>\n",
       "      <th>hit_type</th>\n",
       "      <th>hit_referer</th>\n",
       "      <th>hit_page_path</th>\n",
       "      <th>event_category</th>\n",
       "      <th>event_action</th>\n",
       "      <th>event_label</th>\n",
       "      <th>event_value</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>5639623078712724064.1640254056.1640254056</td>\n",
       "      <td>2021-12-23 00:00:00+00:00</td>\n",
       "      <td>30</td>\n",
       "      <td>event</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sberauto.com/cars?utm_source_initial=google&amp;ut...</td>\n",
       "      <td>quiz</td>\n",
       "      <td>quiz_show</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7750352294969115059.1640271109.1640271109</td>\n",
       "      <td>2021-12-23 00:00:00+00:00</td>\n",
       "      <td>41</td>\n",
       "      <td>event</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sberauto.com/cars/fiat?city=1&amp;city=18&amp;rental_c...</td>\n",
       "      <td>quiz</td>\n",
       "      <td>quiz_show</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>885342191847998240.1640235807.1640235807</td>\n",
       "      <td>2021-12-23 00:00:00+00:00</td>\n",
       "      <td>49</td>\n",
       "      <td>event</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sberauto.com/cars/all/volkswagen/polo/e994838f...</td>\n",
       "      <td>quiz</td>\n",
       "      <td>quiz_show</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>142526202120934167.1640211014.1640211014</td>\n",
       "      <td>2021-12-23 00:00:00+00:00</td>\n",
       "      <td>46</td>\n",
       "      <td>event</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sberauto.com/cars?utm_source_initial=yandex&amp;ut...</td>\n",
       "      <td>quiz</td>\n",
       "      <td>quiz_show</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3450086108837475701.1640265078.1640265078</td>\n",
       "      <td>2021-12-23 00:00:00+00:00</td>\n",
       "      <td>79</td>\n",
       "      <td>event</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sberauto.com/cars/all/mercedes-benz/cla-klasse...</td>\n",
       "      <td>quiz</td>\n",
       "      <td>quiz_show</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  session_id                  hit_date  \\\n",
       "0  5639623078712724064.1640254056.1640254056 2021-12-23 00:00:00+00:00   \n",
       "1  7750352294969115059.1640271109.1640271109 2021-12-23 00:00:00+00:00   \n",
       "2   885342191847998240.1640235807.1640235807 2021-12-23 00:00:00+00:00   \n",
       "3   142526202120934167.1640211014.1640211014 2021-12-23 00:00:00+00:00   \n",
       "4  3450086108837475701.1640265078.1640265078 2021-12-23 00:00:00+00:00   \n",
       "\n",
       "   hit_number hit_type hit_referer  \\\n",
       "0          30    event         NaN   \n",
       "1          41    event         NaN   \n",
       "2          49    event         NaN   \n",
       "3          46    event         NaN   \n",
       "4          79    event         NaN   \n",
       "\n",
       "                                       hit_page_path event_category  \\\n",
       "0  sberauto.com/cars?utm_source_initial=google&ut...           quiz   \n",
       "1  sberauto.com/cars/fiat?city=1&city=18&rental_c...           quiz   \n",
       "2  sberauto.com/cars/all/volkswagen/polo/e994838f...           quiz   \n",
       "3  sberauto.com/cars?utm_source_initial=yandex&ut...           quiz   \n",
       "4  sberauto.com/cars/all/mercedes-benz/cla-klasse...           quiz   \n",
       "\n",
       "  event_action event_label  event_value  \n",
       "0    quiz_show         NaN          NaN  \n",
       "1    quiz_show         NaN          NaN  \n",
       "2    quiz_show         NaN          NaN  \n",
       "3    quiz_show         NaN          NaN  \n",
       "4    quiz_show         NaN          NaN  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hits.hit_date = pd.to_datetime(df_hits.hit_date,utc=True)\n",
    "df_hits = df_hits.drop(columns=[\"hit_time\"])\n",
    "df_hits.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "539ea2a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "session_id                     object\n",
       "hit_date          datetime64[ns, UTC]\n",
       "hit_number                      int64\n",
       "hit_type                       object\n",
       "hit_referer                    object\n",
       "hit_page_path                  object\n",
       "event_category                 object\n",
       "event_action                   object\n",
       "event_label                    object\n",
       "event_value                   float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hits.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec93dd61",
   "metadata": {},
   "source": [
    "### Проверка количества пропусков"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdb6c13d",
   "metadata": {},
   "source": [
    "проверяем пропуски в df_sessions и df_hits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "091d55a8",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IjY2lRo0atGvX7pFQQ+nSpenfv3+SSg0iIiL/BAUaRERERERERERERERE/uO2bdtGp06dKFGiBH369CFPnjzGe+vXr2f+/Pn88MMPNGnShC5duuDp6cnx48cZPXo0e/fuJSQkhNmzZ5MyZcpkXAoREXnVaMAiERERERERERERERGR/yjHc68//PADNpuN1q1bG2EGi8UCQM2aNWnTpg3+/v4sWbKEI0eO4OTkRJ48eejZsyfBwcHUqFFDYQYREfnHKdAgIiIiIiIiIiIiIiLyH2W324mPj2f//v0AZMmSxXjP2dnZCDy8/vrr1KlTB4vFwuTJk7l//z5ms5mgoCBmzJhB69atjemJiIj8UxRoEBERERERERERERER+Y9ycnLC1dUVPz8/TCYTt2/fBv6ozmAymbDZbAC89957ZMiQgXv37uHu7m68nzp1auBBmMFkMj3/hRARkVeWAg0iIiIiIiIiIiIiIiL/AY+rnmCxWLBYLGTJkgW73c7MmTOBB9UZrFYr8CC04PhuQkIC9+/fJz4+/pHpKcwgIiL/NAUaREREREREREREREREXnFWq9UIHMTExHDhwgViY2NJSEjA2dmZxo0bkzZtWjZt2sTYsWMBMJvNWCwWTCYTJpOJ/fv3c/PmTcqWLYubm5uGlxARkX+dAg0iIiIiIiIiIiIiIiKvMKvVitlsBmDx4sW0b9+eatWqUa1aNVavXk1cXByZMmViyJAhpEyZkunTpzNo0CASEhKMEMT+/fuZNm0azs7OlCtXDngwXIWIiMi/yWRXfE5EREREREREREREROSVZLfbjVDCmDFjmDFjBl5eXmTKlAkPDw+aN29O5cqVcXFxIT4+no0bNzJ06FCioqIIDg7mtddew93dnZ07dxITE0Pfvn1p3rx5Mi+ViIj8VyjQICIiIiIiIiIiIiIi8oqbP38+w4YNo3z58nz00UcEBAQQGRlJqlSpcHZ2Ji4uDgA3Nzd++OEHhg0bxoULF4iMjMTLywt/f3/eeecd6tWrB4DNZlOFBhER+dcp0CAiIiIiIiIiIiIiIvIKu3TpEu3atSMmJobPP/+cvHnzGu9FRERw+PBhduzYgYeHB+3ataNYsWJERUVx8+ZNzp07R5YsWfD29sbPzw9QmEFERJ4f5+SeAREREREREREREREREfn3xMbGcvbsWRo0aEDevHlJSEggMjKSVatWMWXKFKM6A8ChQ4eYO3cu+fPnx8fHh5w5cyaZlt1uV5hBRESeGwUaREREREREREREREREXmE2mw2TycT27dupVKkSBw4cYP/+/Rw8eBCAtm3bUqhQIbZv387SpUv58ssvyZs372ODCyaT6XnPvoiI/Icp0CAiIiIiIiIiIiIiIvIKCwgIoFatWqxatYp27dqRkJCAj48PlStX5p133iE0NBSAAgUKsHLlSiIjIxVcEBGRF4ICDSIiIiIiIiIiIiIiIi85m82WpKJCbGwsdrsdDw8PAIYPH46vry/nzp3DZrPx/vvvky1bNvz8/IzvfPfdd1gsFvLly6dAg4iIvBAUaBAREREREREREREREXmJWa1WzGYzAOvWrePAgQP89NNPODk5UaZMGfLmzcvrr79O7969sVqtAJjNZhISEoxpHDx4kFmzZuHp6Un+/PmTZTlEREQeZrLb7fbkngkRERERERERERERERF5ena73aimMHbsWKZPn46TkxN+fn5cv34di8WCu7s777//Ph9++KHxvcOHD7NixQoqVqzIlStXWLhwIb/99hv9+/fnvffeS67FERERSUKBBhERERERERERERERkZfcnDlzGDFiBKGhobRv3578+fPz22+/cfToUQYOHAjAkCFDaNCgATExMXz66aesXbvW+L6Xlxddu3aladOmwKNDWIiIiCQHDTkhIiIiIiIiIiIiIiLykrLb7Vy5coWVK1eSNm1aevbsSWBgIACBgYFs27YNgLJly1K0aFHu3buHl5cXn376Kfny5ePmzZukSZOGvHnzUrhwYUBhBhEReXEo0CAiIiIiIiIiIiIiIvKSMplM3Lhxg1OnTtG0aVMjzAAQFhZGWFgYoaGh9O3bF7vdTqdOnWjTpg0lS5akWbNmj0xPYQYREXmR6IwkIiIiIiIiIiIiIiLykrBarcbf8fHxAMTExADg4uJivJc4zNCtWzdy5szJmjVr+O6777hw4cKfTl9hBhEReZGoQoOIiIiIiIiIiMgLzGq1Yjabjf+32+2YTKZknCMREUkuNpvNOCfMnz+f+/fv07x5c9zd3QHYu3cv9+/fZ+7cuUaYoXv37gQHBwOQMmVKAH799VdjegowiIjIi0xnKRERERERERERkReYo+Nq6tSpHD9+XGEGEZH/MEf4YPz48QwdOpSIiAji4uIoVKgQlSpV4tdff6Vly5ZMmDCBsmXL0q1bNyPMAGCxWAAoXLhwkumJiIi8qFShQURERERERERE5AW3ceNGJkyYgNVqJTAwUE/Uioj8xyQ+7p89e5a1a9dSunRpOnToYFRdqFevHqdOneLIkSPkzJmTZs2aERISYkzjwIEDLFu2jHTp0pEtW7ZkWQ4REZGnpUCDiIiIiIiIiIjIC65w4cJkzpyZbdu28cEHH+Dq6qqhJ0RE/kMcYYYpU6ZgsVi4e/cub775JoUKFcJiseDs7EyZMmU4efIky5Yt4+rVq2zfvh0vLy98fX356aefCA8P5+LFiwwZMoTAwMBkXiIREZEno0CDiIiIiIiIiIjIC87b25uCBQuybt06Fi5cSMuWLRVmEBH5jzl+/Dhz5swhKioKgLi4OODB0ERWqxUPDw9atGhBihQp+PLLL1m0aBHLly/H2dmZ+/fv4+Hhwccff0yDBg0AFIx7Qlar1Rj+SUREnj+T3W63J/dMiIiIiIiIiIiISNLOJcffjv+eOHGCxo0bU6RIEaZMmYKLi0syz62IiDxPVquV9evXs2TJEg4ePEiePHkYMWIEQUFBxvtms5n4+HguXLjAmjVrOHnyJLdu3aJcuXIUKlSIUqVKAWjooieUOMywdetWChcuTOrUqZN5rkRE/lsUaBAREREREREREXkB/L8nQK1WK1arlQEDBvDVV18RFhZG5cqVn/MciohIcnGE2xyhhrlz5/LTTz/x5ptv0qZNG7Jnzw48Pqjw8PlFYYanN2bMGGbMmEGTJk0YOHBgcs+OiMh/is5YIiIiIiIiIiIiycxutxudTePHj6dz586cPn2aO3fuAA/Kibu6ulKhQgUAVq5cSXR0dHLNroiIPGeOij1ms5maNWvSokULcufOzdq1a5k7dy7nzp0DMIIKiZ9lfTgspzDDX7NarcbfR48eZcaMGYSGhlKlSpVknCsRkf8m5+SeARERERERERERkf+yh8tZz5w5E4vFwpEjRyhQoABNmzalaNGiuLi4UKNGDdasWcO+ffu4dOkSgYGBetJWROQ/InGooUaNGphMJsLDw1mxYgUALVq0IFu2bMZn5dk5zsurVq0iJiaGtGnT0rNnTwIDA5MMDyUiIv8+3emIiIiIiIiIiIgkk8SVGcLCwli6dCnbt2+nVatWZMyYka1bt9KyZUv69OnDsmXLAKhZsyZxcXFMnjyZ+Ph4hRlERP5DEocaqlevTtu2bcmRIwcrVqxgzpw5RqUG+fuWL19O3759mTlzJlarFTc3N0BhERGR500VGkRERERERETkuXt4LGeR/6LElRUmT55MWFgYnp6eWK1WunTpQnx8PCtXrmTDhg1s2rSJr7/+mh07dlCtWjUyZ87MqVOnuHDhAv7+/qrSICLyH/JwqAEgPDyc1atXY7VaadGiBTlz5kzmuXz5NWjQgLVr17J//37c3d05duwYOXLk0DlXROQ50xFXRERERERERJ6Lc+fO8cknnwCPjuWceJxnkf8Cu91udIbExsZy6dIl/P39WbhwIRkyZMDV1RUvLy+aN29OWFgYM2bMoESJEnz//fcMHz6c6Ohozpw5w7p16wCNhy4i8l/zcKihXbt2ZMiQgWXLlnHt2rXknr2XXnx8PADz5s2jTJkyxMbGMmrUKE6cOIGTkxNWqzWZ51BE5L/DZFeLgYiIiIiIiIg8B6dPn6ZWrVpUq1aNiRMnArB161YqV66czHMmknwGDx7M5s2bSZEiBZUqVaJ3797AHyEfR4eVyWQiKiqKixcvMnfuXA4cOMDly5fx8/Nj+vTp5M6dOzkXQ0REkonjHGG1WlmzZg1ms5k6deok92y9VJ6k4sIHH3zAt99+S7p06Zg/fz7Zs2dXxTERkedEgQYREREREREReS4uXbpEpUqVAHjjjTdIkyYNc+bMYdiwYdSvXz+Z507k+bPZbLRu3ZrvvvsOZ2dnSpQowciRI0mbNu1ffvfIkSNs376d8PBwhg8fzptvvml0aomIyH+L4/if+DygYRGeTOJQwuHDh7l8+TJnzpzB19eX8uXLkzZtWjw8PIA/Qg3p06dn3rx5CjWIiDwnCjSIiIiIiIjIf8L/a9RVJ+C/z7H+z549S+PGjbl9+zYAlStXplevXmTNmjV5Z1DkOXMcd+Lj4+nVqxcbN24kVapUDB48mCpVqvzp9xJ3nPz66680a9aMNGnSsHjxYry8vJ7X7IuIyAsm8flB17ZPJvH9QVhYGPPnzycqKsp4P23atDRq1IiKFSsSEhICQJs2bdi1a5dCDSIiz5HieSIiIiIiIvLKs1qtRmPl/v37Wb16NcuXL+fYsWPAHyXd5d/j5OSE3W4ne/bslCpVyng9NjbWCDNYLJbkmj2Rf93DxxhHeXBXV1dGjx5NtWrVuHXrFkOHDuXQoUN/Op3EHSa5cuUiODiY33//PUkHjIiIg+PYo+ucF4vNZnvs61ar9Zmn5zg/7Nixg+PHj//pvyF/cNwfTJo0ibCwMDJmzMinn37KxIkTadKkCZ6enkydOpVZs2Zx6tQpAKZNm0bZsmW5du0a77//PmfOnFGYQUTkX6ZAg4iIiIiIiLzS7Ha70cgYFhZGmzZt6NOnDwMGDKBnz57MmjULeNC5qIbff5fJZOLMmTP8/vvvFCpUCE9PT3bv3k2XLl0AcHZ2fuaGfJEXmdVqNZ6UtVqtXLt2jcjISKOD0cXFxQg1XLlyhR49enD48OEnmvb9+/dJSEggOjr6X5t/EXl5RUZGAgpvvkgSB21/+eUX9u7dy86dO4mPj8dsNj/1dkpcZWDFihV06tSJMWPGkJCQ8I/P+6to3759zJo1i/z58zNixAgaNWpEtWrVGDhwIIGBgVitVmJjY0mZMiVxcXEATJ8+nddff53Lly/z5ptvEh0drf1LRORfpECDiIiIiIiIvNIcnYhTpkwhLCyM1KlT884771C8eHEuX77MhAkTmDp1KvDgKS2FGv5ZDzfuZs2alUGDBjFr1izWrl1LypQp2bRpkxFqMJvNCjXIKyVxGeply5bRoUMHKleuTJUqVahXrx4zZ87k+PHjSSo1XL58me7du//fUENCQgIff/wxBw8eJHPmzPj5+T2vRRKRl8SePXsoV64cq1atAhRqeBEkrqQwefJkWrduTcuWLWnbti1NmjTh3LlzTzVUxMNhhgkTJuDh4UGPHj1wc3P7V5bhVXPq1CliY2N5//33yZMnj/H6lClT2LRpE2XLlqVPnz4kJCSwYcMG415hypQpFClShObNm+Pt7a0hPkRE/kUKNIiIiIiIiMgrKXGn+NWrV1m/fj1FixZlypQpfPLJJ4wePZqePXvi5OREWFgYU6ZMARRq+Cc9/FR6ZGQkZrOZwMBAPDw8yJgxIwsWLFCoQV5ZiSvEjB49moEDBxIREUFwcDBZsmThzJkzjBs3jqFDh3LgwAEj1FC9enUj1HDkyJHHTttms+Hr60uVKlWYNGkSqVKleo5LJiIvg0OHDmG1Whk0aBDr1q0DFGpIbo7wQVhYGJMmTcLZ2ZkKFSqQLVs2fvzxRzp27Mjx48efaFoPhxnGjRtHbGwsCxYsICgo6F9bhleNYyiJjBkzGq+FhYUxceJEQkND6dKlC2nSpKFjx46MHDmS27dvG/vQwoUL6dq1K/Dnw4iIiMjfp0CDiIiIiIiIvJIcnYjfffcdFy5c4LfffqNx48bGk1d+fn7Ur1+f3r174+zszBdffJEk1KDG/r8n8ROIixYtonPnztSsWZM+ffqwZs0a43O5c+dm4cKFjw01XLhwIVnmXeSf4gj0zJs3j5kzZ1K+fHnmz5/PkiVLWLZsGaNHj6ZMmTJEREQwfvx4jh49iqurK6NGjaJGjRpcvnyZTp06ceDAgSTTtdvtuLm50aVLF0aNGoW/v39yLJ7IY/3yyy9cvnw5uWdDgE6dOtGpUyfi4uLo3bu3Qg3JKHFQ88KFCyxatIhSpUoxY8YMpk6dytKlSylVqhS//vor3bp1+8tQw+PCDPHx8SxcuDBJlQH5a+7u7gDGdWdYWBhhYWGEhobSvXt38ubNi6urK+7u7ty6dYtbt249MlSd3W43toeIiPzzdIQVERERERGRV9by5ct5//33GTVqFOnSpSMwMBDAGFPY09OTunXrPjbU8HBDpTwdR6Pu2LFjGTx4MLt27eL27dusXbuWESNGMH/+fOOzAQEBSUINXbt2Zf369bRu3Zq+ffty9+7d5FqMl4rFYgFQdYsXjON3nyZNGj766CNCQkKwWCy4urpSrVo1evXqReXKlTl48CDLli3j5s2bRqihSpUq3Lhxg7NnzyaZpqMz0sXFBQ8Pj+RZMJHH+PHHH3nzzTfp3LmzQg0vAJPJRMeOHenYsSNWq1WhhmTkCHkeOXKE48ePEx0dTbt27QgICCAhIQFfX18mTZpE+fLl+e233/5vqEFhhqf3uN+647XQ0FBcXFxYt24dI0eOTBJmCA4Oxmaz4ezsjKenJ2az2RjKI3GAQcNNiIj8uxRoEBERERERkVfGwwGE7NmzExoayqlTp7hy5Qo7duwAwMXFxfhsihQpqFu3Ln369MHZ2Znw8HDCwsIA9KTVM0jcYLxhwwZmzpxJ6dKlmTNnDrNnz6ZFixZERUUxbtw45syZY3w2ICCARYsWkSZNGjZs2ECvXr04d+4cuXPnxtPTMxmW5OWybds26tevbwzroTDOi+P69escO3aM0qVLExQUhNVqxdnZGXjQweXv788HH3yAv78/O3bsIDIyEnhwnBo3bhzh4eG8/fbbj0xXnSfyIkqVKhU5cuTgp59+onfv3ly6dCm5Z+k/z2Qy0blzZzp16oTVaqVXr16sXbvWeE+hhudn+vTpNG7cmGXLluHj40OuXLmwWq24uLhgtVrx9vZm7NixVKhQwQg1/PLLL0mmoTDD00s8BFp0dDTnzp0jKiqKuLg44ME1aFBQENu3b2fu3LmUK1eOnj17EhwcDDy4H9i7dy+HDx+mSJEi+Pr6JteiiIj8Z6llRkRERERERF4JiUu9Xr16FYBixYrRoUMHihcvjtlsZsuWLezbtw940DiZONRQp04d+vbtS0JCAmFhYRw9ejR5FuQlZrPZknSyHj58mJQpU9KrVy+KFi1KqVKl+OCDD+jWrRvx8fFMnDgxSaghV65czJs3j9dff52KFSvy2Wef0bJlS+DxT9bJg/Vis9mYPHkyJ0+epHXr1kRGRib5fcvzk/h36lj/d+7cAR5UaoiPjzee0k2sQIECFClShBs3bhjBK4vFgouLC+XLl08yPZEXlc1mI1OmTMyYMYOQkBAiIiLo06ePQg0viI4dO9KpUydsNhu9e/dWqOE5s9vt5M6dm0yZMvHtt98SExPDnTt3MJvNWK1W479eXl6MGTPGCDX06tWLn3/+GVCY4Vk41i3AwoULadeuHdWqVaNmzZosXLiQhIQEXnvtNT755BNSpEiBzWbDarWSPn167t27B8Du3buZOHEicXFxNG7cGC8vr+RcJBGR/ySTXVcrIiIiIiIi8goZO3YsR48epUePHuTLlw+AgwcPMmXKFHbv3k3ZsmVp3749hQsXBpI2Dt+7d4/ly5fj7OxM06ZNk20ZXnYjR44kNjaWS5cukTp1akaMGEFCQgIuLi4AxMTEsHz5csaMGYOrqytdunShRYsWxvfj4+MxmUzG5xNvI3m8u3fv0qpVK44cOULu3LmZM2cOqVOn1rp7jhKv68S/9+vXr1O/fn1SpkzJlClTyJo162M/u3r1avr06UOXLl1o3759si2HyN8RHx+Pq6srFy5coHv37hw9epTChQszevRoMmXKlNyz959gt9uNkIIjZOj422azERYWxuTJk3FycmLkyJG88cYbST4j/x6r1UpERATDhw/nxIkTFC9enAkTJiQ5Xzs64GNiYujVqxfbtm0jbdq0rFmzhtSpUwMPhlQbN24cFouFBQsWKMzwJxL/pseMGcOMGTPw9fUlW7ZsuLi40KhRI6pVq4arqysAhw4d4sMPP+TGjRtky5aN9OnT4+7uzvfff09CQgJ9+vQxrle1v4iIPF+6oxUREREREZGXmtVqNf6OjIzk1KlT7Nu3j/nz5xtVFooUKULHjh0JDQ3l22+/ZcqUKRw6dAh4tFJD06ZNjTCDnoh+ehcuXGDnzp0sXryYXbt2cfv2bSDpMB9eXl40aNCAHj16GJUa5s2bZ0zDxcXF6AxOXHlDHs9iseDp6cnMmTMJCQnh5MmTNG/eXJUanjPH73TYsGH06dPHeLLTw8ODYsWKcfr0aWbMmGF81m63G6XGAX7//XdMJpNR4lrkZWO1Wo2OwbRp09KqVSvSpk3LoUOH6Nu3ryo1PAeJS+vfunWL06dP89tvv3H+/HngwbHnww8/pH379qrU8C/6s/Ou2WymUKFC9OnTh4CAAPbv38/QoUO5ffu2cb5OXKlh1KhRFCtWjKZNmxphhpMnTzJv3jzi4+MVZvgLjn1hwYIFzJo1iwoVKjBnzhwWLVrE5MmTqV69Oq6urty7d4979+5RuHBhZs+eTcOGDQGIiIjg0KFDlCpVinHjxhlhhocrkomIyL9PFRpERERE5KklLtsoIpKcEj/lvHbtWk6dOsXWrVu5ePEi8fHx1KlTh2bNmpE3b17gwRAIYWFh7Nmzh3LlytGuXbvHVmqQv2fv3r3MmDGDffv2kT59egYNGkS5cuWApOvZUalhwoQJWCwWunTpQps2bZJz1l9KD5+X69Wrx/Hjx/H392f+/Pmq1PAvS7xu4+PjKVu2LNHR0bz99tv07t0bT09PDh06RJs2bYiJieHdd9+lT58+ODs7G9M4dOgQvXr1AmDKlCkEBAQky7KIPKvE+8GkSZNYunQpCQkJREdHYzabsVgsFCtWjBEjRqhSw78k8TaYO3cuX331FT///DNmsxlnZ2eaNGlClSpVjOueiRMnMmXKFFVq+IclPiefPXuWyMhIrl+/zmuvvUbu3Llxd3fHarWyf/9+Bg8ezJkzZ6hVqxYDBgzA19f3kUoNiSv+OMydO5cyZcrg7++fHIv4Url27Rpt2rQhKiqKSZMmGdXbAPbv38+BAwfYtWsXLi4utGvXjtDQUOLi4oiPj+fy5cv4+Pjg4+ODh4cHoPsFEZHk4vzXHxERERER+YPjqRGAlStX4uPjQ+XKlZN5rkSev//XmKWG4OfHsQ3GjRvHtGnTSJkyJTlz5qRIkSLs3buXNWvWEBcXR5s2bQgJCaFQoUJ06tQJgF27duHk5ETr1q0pWrSoGif/AY7ffqlSpbDb7VgsFvbt28eSJUtIlSoV+fLlM55AdHJyMio1WK1WxowZg6enZ3Ivwksn8Xl5+fLlXL16FbPZjMlk4vTp0zRv3py5c+cq1PAvSdxxtW3bNi5dukTu3LmJiIhg/fr12O12evfuTeHChZkwYQJt2rRhwYIFXLp0ifLlyxMcHMyJEydYsGABFy9eZPDgwQozyEvJcWyZNm0akydPplSpUrRo0QIfHx+ioqIYM2YMERER9OjRgzFjxijU8A9LXM3IUVo/bdq01KtXD4vFwu7du5k9ezb79u2jWbNm1KtXjy5duuDk5MQXX3xB7969MZlM1K5dW9ewf0Pic/KMGTNYvnw5ly5dwmKx4OzsTFBQEL179yZfvnyUKlWKgQMHMmTIEL7++muAJKEGs9mM3W5PUrHKsZ2bN2+ebMv4ons45BkVFcWJEydo2LAh+fLlIyEhgevXr7N69WomT56MxWIxPvvrr78SHh5O/vz5cXNze6T6haqGiYgkH1VoEBEREZFnMnLkSGbPns0777xDjx491Akl/ymJG8oiIiKIiorC1dWVggULkjJlSkChhn/a48aDdnCMO1+2bFm6d+9OYGAggDHswY4dO6hatSpt27YlJCQEeFCpYcqUKezatcvoaEyfPv1zX66X2cMNxjExMdjtdry9vY3X9uzZw9SpU4mIiKBq1ap88MEHxpNxiTvXo6OjuXDhgkrt/w2OUI+Pjw/ly5fn+vXrXLp0ifPnzxMQEKBQw78g8fFo7NixzJkzB7vdTt68eTl79ixxcXHcv3+fhg0b0qtXL7y8vNi7dy/Dhg3jzJkzSTpRvLy86Nq1qzHcjc4h8jK6dOkSzZs3x8nJibCwMHLnzm28d/XqVUaNGsXXX39N4cKFGT16tEIN/wLHNVGZMmXo1auXsQ3Onz9Px44dOX36NI0aNaJr167GNevnn3/OF198AcDQoUN56623km3+XxXjx48nPDycTJkyUaNGDWw2G4cOHeLIkSOkTp2ajz76iNq1a+Pu7s6+ffsYMmQIv/32G2+88Qb9+vUjVapUyb0IL71t27ZRrlw5bt26Re3atXFzc2PkyJF8//33xlASAB07dqRgwYLs2LGDhQsXUrduXYYNG6aKlCIiLxhVaBARERGRJ5K44+ro0aOsXLmSYsWKUatWLYUZ5D/Fbrcb+8LkyZOZNm0asbGxANSqVYuGDRtSokSJP+18l2dz8+ZN0qZN+9j1eeDAAVxcXGjTpg2BgYHG8ap8+fKkT58eNzc3Nm3ahIuLCy1atCBfvnwUKlSItm3bcufOHapWraoww1NKfE5YvXo1e/fu5dixYwCULl2a/PnzU6dOHUJDQ4EH+83mzZsBjFBD4koN3t7eRphBHe5Pb+PGjUybNo2yZcvSrVs3goKCiI2NJTo6mj59+rBnzx7ee+89DT/xD3Mcj5YtW8b06dOpUKEC7du3p0CBApw5c4YjR44wefJkli1bBkCvXr0oVaoUEyZM4PTp0+zdu5eEhARCQkIIDAykUKFCgPYBeXnduHGDixcv0qJFC3Lnzo3FYsFsNmOz2fDz86NPnz7cv3+fbdu20a9fP4YNG6ZQwzOIjY3F3d39se/t2bPHKJ2fOFDy9ddfc+rUKUJDQ3n//feJi4vj9OnT+Pv707FjR6xWK1OnTuXu3bvPazFeWZs3byY8PJzixYvTr18/I2gL8Pbbb/Pjjz+yb98+qlatSooUKShevDgDBgxg2LBhrF27lujoaCZNmoSrq2syLsXLLTw8nPHjxzNjxgzKlClD2bJlWbduHR988AEWiwUfHx+qVq1K48aNKV26NACFChVi6dKl3Lt3T+fgZ6ShSUXk36RAg4iIiIg8kcTDTMTExHD//n3atWtH0aJFk3nORJ4vRwfWnDlzmDRpEhkzZqRo0aLs37+fr7/+muvXr2OxWAgNDVWo4R+yZ88eWrVqRZ8+fWjRokWS92JjYzl69Cg+Pj5GmfbE6z0oKIjmzZtz4sQJvv76a5ydnWnWrBkhISEUKVKESZMmGWEGbasnkzjU4yhr7erqStasWbl69SpLlixhwYIFHDhwgMGDBxMaGorZbCYsLMwINbRp04a8efM+tsFYjchPzvGbPXz4MADvv/8+QUFBALi4uJAuXTrCwsLo378/69ev1/AT/zC73U5CQgLbtm3D1dWVLl26EBQUhN1uJ0eOHOTIkYM8efLQtWtXI9TQu3dv/P398ff3p2rVqo9MU9tFXmb37t0D4Pr169jtdpydHzQ9O0IN6dKlo3///hw6dIh9+/bRp08fhg8fTubMmZNztl8qjuvNZs2a4e/vn+S9qKgoDh48iL+/f5J7tLCwMMLCwggNDaV37944OzvzwQcfULFiRT788EOcnJzo0qULr7/+OgUKFHjei/TKcJyTDxw4AECHDh2ShBmmTZvGjz/+SPny5enatSv379/n2rVr5M6dmyJFitC3b1969epFsWLFFGb4m3LmzImzszMbNmygTJkyjBkzhvTp03Pz5k1iYmJ4//33yZo1K+nSpTO+s3v3biwWC8HBwbofeEaO+4OlS5dSpkwZMmXKpPsrEfnH6A5JRERERJ7Yd999R//+/ZkwYYLRUQIkKZks8qpzjNr37bffkjdvXiZPnsyoUaMYPXo0VatWJSIigvDwcPbs2QP80bkuz+7XX38FHqzzhIQE43XHU0C+vr7cvHmTX375BXjQIZ54vRcuXJjGjRsD8NVXXzF37lzOnj0LYBzH1Nj25Bzraf78+caTb3PmzGHdunUsX76c8ePHAw+eWp82bRoAJUuWpGPHjhQtWpStW7fyxRdfcOTIkeRahJea1Wo1/jaZTNhsNk6fPo2TkxPZsmUD/hjD22q14uHhwYgRIyhYsCCnTp2iWbNmREZG4uTklGRa8vRMJhPR0dEcO3aMTJkyERQUhM1mS/KZ4OBghgwZQrp06fjyyy8ZMWKE0en78GdBgR55ueXMmRMfHx9OnDjByZMnk7zn5OREQkICmTJlokiRIvj4+BAREUG7du2IiYlJpjl+uURGRjJs2DCWLl3KwoULOXPmTJL3rVYrdrudmzdvcuXKFSBpmKF79+7kzp2bkydPcvz4cXbu3GlUGTOZTEaY4XHHJvlrJpOJ2NhYDh8+jI+PD3ny5DHeCwsLY9y4cYSGhvLhhx/i4uLCm2++yZAhQ7Barbi6ulK8eHFWrFhBq1atAHT/8Dfkz5+fEiVKsHLlStavXw88qJI0cuRIPv/8c4oUKULq1KmNzx86dIhZs2aRIkUKo1qSPJs1a9bwySefsHr1agDdX4nIP0Z3SSIiIiLyxHLlykXHjh1JnTo19+7dIzw8nOjoaJydndXgIq+0hzsQ79y5w7Fjx6hVq5bx5FWxYsVo3bo1VapUUajhH9a8eXOmTp3KmDFjcHFxMYY2MJvNuLi4UKFCBex2O1u3biUyMtL4nslkMgJXAQEBeHh48Nprr7FmzRqWLl1qfCbxf+Wv2e12bty4werVq0mVKhU9evSgcOHCAGTPnt0IoJQvX54qVaoQFRUFQKlSpejcuTP58+dn+/bt3L59O7kW4aWVuJTvN998YwQZfHx8sNlsxr7hYDabsVgsuLq60q5dO9zd3fn1119p2LAhN2/eVFngf0DKlClJkyYNCQkJWK1WnJycHjneh4SEUKZMGSwWC5s2bWLs2LHExsY+9rMiL7r/19nt6+tLjRo1+PXXX1myZInRWQ4PAtAuLi7Ag2NZ+vTpCQ0NpV69enh5ef3r8/0q8PX1pWXLloSEhLBs2TLmzJmTJNSQOnVqihUrRnR0NL/99tsjYQbH0E65cuXC09MTk8mkSkn/MHd3d1KmTElCQoJxfJ80aVKS7RASEoKLiwvOzs5cu3bN+JyzszMZMmQAHuxnujb9a3/2YIWfnx9NmjQBHgyNdvXqVeM9JycnDhw4QNeuXdm4cSNz5syhb9++HD16lK5du1KyZMnnMu+vKn9/f5ydnfnmm2+SrHcRkb9LVyciIiIi8pccDZfp06enUaNGvPnmm6RNm5Zdu3axbNky7t27pw5beWUl7kBcv349EyZMYObMmaRLlw4fHx8A4uLigAdPAynU8M9yhEkqVKhAqlSp+OSTT2jQoAHLly83PlO6dGkCAwNZtWoVW7duTfKkp2PbXbx4EScnJ1q0aEHWrFmZPXs2O3bseK7L8rJxHPsfforfZDIRGRnJzz//TIUKFZKUUw4LC2PixImEhobSq1cv4EHJ5a1btwJQokQJOnfuzIQJE6hQocLzWZBXiOP3PHr0aDp27MjGjRsBqFixIk5OTqxdu9YYssCx/RzfSZcuHRaLhWzZsnHx4kW+/fbb5FmIV4jVajWeOL948SLz5s0DSLL+bTYbnp6exhOfCQkJLFu2jHnz5mGxWNRhJS8VR2gHYPv27cycOZNJkyaxadMmANzc3KhevToZM2Zk8eLFTJgwgRs3bgAYw08cOHCAgwcPUr16dcLDw2ndujWgp9H/it1ux8nJidq1a9OqVSv8/f1ZuXIls2fP5rfffjM+V6hQIWJjY+nUqRNhYWFUqFCBjz76yAgzAPzwww/cvXtXQxv8w6xWK1arlZw5c3Lv3j0WLVrEjBkzmDx5cpJQSeLhWGJiYpIEfxwUKnkyjvU4Y8aMRzrQK1WqRKNGjdi7dy/Hjx8HHpyT79+/z8qVK9m8eTMfffQRI0aM4NatWwwcOJD33nvP+Jw8PbvdTkhICO+99x4///wzR48eTe5ZEpFXiHNyz4CIiIiIvHgeHr858d/p06fn7bffBmDevHnMmzcPV1dX3nrrLVKkSKGy7fLKcXQGjh8/nvDw8CTvbdmyhfr16+Pm5kZCQgIuLi5GqMHxvmPc6LJly2rfeAYPP0HueHJtwIABmEwm3n77bQIDA6lbty6TJk1i1KhRREdHU6lSJbJnz47JZOLw4cMsXryYTJky0bx5c8xmM5999hknT55Up/qfuHz5MsOHD2fYsGF4e3s/8r7FYsFutyfZPomfBO3WrRv+/v5Mnz6dgwcPUqlSJeNzoaGhxt8Pn2/k8RIHq/bv38/MmTMpWrQo+fPnBx6EqQICAti6dSuDBw9m0KBBj6zXM2fO4OnpaewjZcqUee7L8bL6s9+p2WwmRYoUNGvWjO3btzN37lz8/PyoWbOmUV7f8UT6tWvXSJ06NZ07d2bq1KmsXbuWWrVqkSlTpue9OCLPJPExf+LEiUyZMsV4z83NjT179jB48GBKlixJ7969GTRokFFBIDQ0lCJFinDy5EkWLlxIdHQ0OXLkMDojdf/w10wmk3FMqVmzJi4uLkyePJk1a9ZgNptp2rQpuXLl4p133uH7779n06ZNeHh4UL16dfLly2dM58CBA8yYMQNPT09KlSqVjEv08vp/5wSAWrVqsWjRIsLCwgAoV64cPXr0IHfu3MCDbXnkyBFu3rxpVCjRPvDsFi5cyJgxY/D19aVs2bLUq1fPuNasVasWGzduZNSoURQoUABfX188PDz49NNPyZcvH7dv3yZVqlQEBgYawUNdmz47x2+4aNGizJ49m+nTp1OoUCHSpk2bzHMmIq8CBRpEREREJInEnSZ79+7lxIkTnDp1irRp05IvXz5KlixJhgwZaNCgAQBz585l5syZAAo1yCsl8e946dKlTJs2jeDgYCpVqsS5c+fYsWMH27dvZ+TIkfTu3RsXF5dHQg3Ozs6sX7+eqKgo/P39yZgxYzIv1cvL0bjYvn17PD09GTZsGB9//DF2u50GDRrQsmVLYmJiWLhwIePHj+err74iNDQUi8XC5s2buXr1Kn369AEgKCgIQGN2/x9OTk5s2bKFK1eusGDBAtzc3Fi5ciVvvfWW8T48OE9cv36dlStXPrasdapUqQCMp0cTn2MST0f+P8c6W7duHe7u7qRKlYq+ffsSEhKC1WolS5YsDBo0iBYtWhhl3j/44AMyZsyIh4cHBw4cYMGCBZjNZjJkyJBknHRtg/8v8W/28OHDnDlzhkuXLpEyZUoKFy5Mzpw5KVWqFG3btmXatGlMmjSJe/fu8fbbbxthhsOHD7Nx40YCAwOpUKECv/zyC8uWLePw4cMKNMhLw3FNNHPmTKZMmYK/vz81atTg6tWrbN68mWXLlhEfH8+IESOoVq0abm5uzJ49m/3797Nz505jOh4eHvTv35+aNWs+Mm35c1ar1TimfPfdd5w8eRKLxUJsbCzr1q0DMEINEydOpFWrVuzZs4dRo0Zx/PhxMmfOzO3bt1m6dCnXr1/n448/ply5csm5SC+lJzknFCxYkP79+zNkyBDsdjtZs2Y1wgwA33//PZMnT8ZsNlOlShVA+8DfUaRIETJmzMjly5fZvHkzmzZtol27djRq1IjixYvzxhtvsGDBAtauXUuTJk2w2+24uroaQ1IkpuuiJ/Pw9byDxWLB2dmZSpUqUb16dbZv387p06dJmzbtn35HRORJKdAgIiIiIgabzWbcZDrK6ickJCT5TMGCBRk9ejRZsmShUaNGwINKDQo1/HMc607rMPk83Jh18uRJMmfOzLBhwwgMDCQyMpKSJUsyfPhw5syZg6urK127dn0k1NCiRQvu3LlD2bJlFWZ4Sv+vUkyzZs2w2WyMGDGCAQMGYLfbadiwIZ07dyZbtmxs376dDRs2cPLkSQBee+01BgwYQNOmTQFYu3Ytzs7OFCtW7Pku1EvE29ubdOnScezYMZo3b05ISAgLFy7k5s2btGnThsDAQN566y1WrlxJ27Zt+fnnnylbtixdu3ZNUtb63r17AMa6VkPms1u1ahV9+/bFy8sLs9mc5Gk3m81GoUKFmDlzJh999BGrV6/m4MGDvPbaa/j6+rJv3z6ioqL4+OOP8fPzM76nRvv/L/F10eeff87s2bON3zSAj48PoaGhfPLJJ7Rt25bo6GgWL17Mxx9/zKFDh8iTJw9xcXGsWLGC8+fP89577/Haa69RtGhRli1bZgxXJPIiS9wJFRMTw44dOwgODmb48OHkyZOH+Ph4qlevTo8ePVi9ejV2u52RI0dSoUIFcuTIwZUrV9i2bRuxsbEEBASQO3duihcvDqjz8Eklro4xduxYpk+fjpeXF/7+/gQEBHDq1ClWrlyJ1WqlWbNm5MqVi5kzZzJ48GB27NjBnDlzjGllyZKFIUOGGOF0bYMn9yTnhNKlSzNw4ECaNGlCTEwM48aNY8GCBVy5coWMGTNisVhYu3Yt0dHR9OvXj4oVKybX4rwS7HY7mTNnpnLlyqxbtw5/f3/u3bvHpEmT2LVrF82aNeOjjz7i4MGDfPXVVzRq1AhXV9c/7VzXvvDXEh+PRowYgbOzM6GhoZQqVcqougPwxhtvsGnTJubOnUuJEiV0DyAif5vJrgHKREREROQhU6ZMYeLEiRQtWpQPPviAVKlSce3aNWbNmsWhQ4fIli0b4eHhZM+enevXr7N06VLmzZuHt7c37733Hg0aNMDT0zO5F+OllLhx5fbt29y6dQtnZ2ecnJySPMWpsMPzMW7cOO7cucPu3bupUqUKvXv3Nt6LjY1l/fr1DB8+nOjoaNq2bUvXrl0BkpQZv3nzJmnSpAG03Z5U4v3gp59+4vz585jNZvz9/fH39zc+N2fOHEaMGAHA4MGDadiwofHesWPHiImJwcXFhddee83YfxYsWMDYsWPJlSsXU6ZMUQnUx3Cs/3v37vHuu+/y888/A/D6668zYMAAI5yzZ88eRo4cycmTJ8maNSt9+vRJ0jB/4MABPv74Y+7cucO0adPImzdvsizPq+LChQsMGTKEgwcPcv/+fbp3707Tpk1xd3fHbrcb46sfP36cFStWsHv3bs6ePYubmxtBQUE0btyYevXqAToWPS3HcCoFCxakWbNmeHl58dtvv/Hll19y8uRJQkJCmD9/PrGxsWzevJkxY8YYFWDMZjOenp506tSJZs2aAdCrVy/Wr1/PokWLjGFDRF50a9aswcPDgx49etCrVy8jJOg4nuzbt4+uXbsSGRlJ3bp1GTly5P+dnjrSn96XX35Jv379KFeuHN26dSMwMBCr1crq1auNINvbb79thBrgQYWkEydOcPv2bbJnz07GjBnJli0boG3wrP7qnBAcHMy8efPw8vJiy5YtDB8+nBs3bhAfH4+rqys5c+akefPmvPnmm4C2w5P6f0/4Hz16lPbt25MvXz7q1q3L6dOnmT17Nnfv3qV8+fL4+fmxbNkyOnTowIcffvic5/zVkXgbLF++nAEDBgDg7OxMjRo1aNKkCdmzZydVqlTcunWL999/n+PHjzN79mxKliyp608R+VtUoUFEREREkvjhhx+YP38+uXLl4uOPPyYwMNB479ixYxw7dow0adLg7OyM3W4nXbp0NGjQAJPJxPTp05k4cSIlS5ZM8j15MokbCObPn8+aNWv48ccfMZvNuLm58e6771KpUiXy58+vCg7PwdWrV5kzZw5OTk64ubkRGxsLYDRGuru7U7NmTUwmE8OGDSM8PBzgkUoNCjM8ncT7wbRp05g3bx43btwAIFu2bAwaNMgY87lFixbAg6eDBg4ciMlkMp44TDxe9KFDhzh16hTffvst69atw9PTkxEjRijM8CfMZjNWq5UUKVIQEhJiBBouXbqUpNJIyZIlefvtt1m4cCG///47GzZswGw2ky5dOk6cOMH06dM5e/YsgwcPVpjhb7LZbGTJkoWBAwfy2WefsWPHDlavXk1wcDDFixc39hm73U5gYCB9+vQhNjaWS5cukSJFClKmTImPj48xLXWcPLn9+/czd+5cgoKCGDRokHF9U65cOS5cuMDZs2dxcXHh9u3bZMyYkcaNG1OsWDHOnj3Ljz/+SEBAABkzZqRgwYLAg1DV1q1bKVKkiNGpKPKi++abb+jVqxcBAQGkTJnS+O0mDnCWKFGC8ePH07VrV7766iucnJwYPnw48EcZ8sR0HHp63333He7u7nTu3JnAwEBj/derV4+AgAA+//xzVqxYgclk4r333iNXrlzkzJmTnDlzPjItRwhOns6TnBNcXV25ffs2KVKkoEqVKgQHB3Pz5k1Onz6Nv78/adKkMYK2Oic/Oce1TlhYGKlSpaJUqVLGbzt//vy0b9+ezz77jIoVK9KpUycqVarE8OHD2bdvn3Eft3XrVipUqKAw4TNIXJlh2rRpnDlzhsGDB3P//n2mT5/O2rVr2b17NwEBAbRv355SpUrRt29fPvjgA7Zt20bJkiV1Lywif4sqNIiIiIhIEl9//TU9evRg4MCBvPPOO8brkyZNYvLkyYSGhjJo0CDu3bvHypUr+fDDD/Hy8uL69evMmTOHzJkzJ/mePJnEnd1jxoxhxowZpEmThpIlS2KxWNizZw8xMTEUKFCAZs2aUatWrWSe41fP4576+fHHH+nevTvnzp0jICCARYsW4e3tnaRhPjY2lg0bNjBs2DDu379PixYt6NGjR3Iswksv8X4wevRoZs6ciY+PDyVKlODOnTt8//33+Pj4MHr06CTjPieu1DBs2DDq168PPOhoOXDgAC1btgQePD0UEhLCsGHDklR6kMe7dOkSw4YNIzY2losXL3Lu3Dny5cvHrFmz8Pb2Bh7sN19++SVffvklhw8fBv4IRHh5edG1a9dHnuKV/++vOjcuXLjA0KFD2bFjB/ny5aNv374ULFgQJyenvxyySNvgz/3Zulm2bBmffPIJI0aMoG7dusbrjid0HddF8fHxbNmyhebNm+Ph4ZFkGufPn8fLy4tZs2axYsUKnJycWLBgwWM7GUVeRGfPnmX+/PmsW7eOqKgoGjRowJAhQ4BHj1mJKzW88cYbjB49Orlm+5USHR1tBGnXr1+Pu7s7ZrM5yXHrwIEDDBo0iEuXLvHmm2/StGlT43pHx/9/xj9xTnDQNnl627Zto0OHDphMJgoUKJCk+lRMTAwdO3bk6NGjLFmyhDx58hAZGck333zDxo0b2bNnD/DgwQENO/d0Ev9Wp0+fztixYwkICGDq1KlkypSJS5cusXHjRnbs2EFERATOzs6UKVOGwoUL8+OPP7J3716mT59uhDtFRJ6FAg0i/yG//fYbKVKkIEOGDMk9KyIi8gJzdCKOGTOG2rVrA0kbaLp160ZISAgtW7bkyJEjzJo1i0KFCgEPOnbd3d0BPW3yrBxjpJcpU4ZevXqRO3duAE6fPk3nzp05f/48jRs3pkuXLkaHovyzvv32W8qWLWv8/88//0zXrl05d+4cpUqVYsqUKbi7uz8Sati4cSOffPIJcXFxLF682Ngv5OktWLCAzz77jAoVKvDRRx8RGBhIZGQkffr0YdeuXfj4+DB27FjKlCljfCdxqMExdjHA5cuX2bBhAzdu3KBIkSIULFhQlRn+xOMa1s+ePYufnx+urq7Uq1ePU6dOkS9fPmbPno2Xl5fxvcuXL7N161Z+/PFHIiMjKVu2LCEhIUaDsc4JTyZxsOrYsWNcuHCBixcv4ubmRunSpUmfPj0+Pj6cP3+eYcOGPTbUIE/n+PHjxMfHkz9/fqMCD/yxLT7++GNWrFjB7Nmzjeowia+LunfvTnBwMO+++y6XL19m9uzZxtPrFouFL7/8koEDBxr/XnBwMKNGjTLKwYu8aBKfCxIfu8+ePcuiRYv48ssvcXd3p2vXrtSrVw+z2fzIMX7//v106NCBmJgYZs6cSWhoaLIsy6vmvffe4/jx43z55ZdkyZLlseftYcOGMW/ePHx8fKhSpQotW7ZUiPMZPe7apX///qxcufKZzgnyz9iyZQvffvsty5YtA+DNN9+kbt26lChRgu3bt9OhQwdq165Nv379SJ06NVarlfj4eCZOnEiGDBmMCm/yZBIfZ65du0a3bt2Ii4tj0KBBhISEPPJQwNKlS9m7dy8bN24EwNXVFavVSo8ePWjZsuX/HTpEROT/0ZATIv8RR48epXHjxtSoUYOePXsq1CAiIn8qa9asAJw6dQqAKVOmPNJAY7PZSJEiBffv3+fKlSvGdx1hBlAZ2Wdht9vZvXs3bm5utG/f3ggzAGzatInffvuN0NBQmjdvTnx8PGfPniV79uzJN8OvIEeD5AcffED37t2BB51PjhLKe/fupVu3bowfPx43Nzcj1ODu7k716tWJi4vDZrMpzPA3XLhwgZUrV5I5c2a6dOlilPJNSEjg2rVreHl5ERUVRdeuXRk/frwRamjRogU2m41Ro0YZY9cDZMyYkVatWiXLsrxMEjcuxsbGcvv2bTJkyJDkGDNv3jyaNWvGsWPHaNmyZZJQQ6ZMmWjevDnwaAeAwgxPxmazGdtg8uTJzJs3j9u3bxvv+/n5UaxYMbp27UrWrFnp378/ADt27GD48OEKNTyjvXv3MmHCBGbNmkWRIkWAB095On7bjmGDTpw4QalSpZg8efIj10UxMTFYLBYuX77MpUuXjM4rZ2dn8uTJQ/Xq1fH19SU4ONgYy1vkRfTw8TouLs54ujx79uy888472Gw2li9fzuzZs3F2dqZ27dqPhBqKFy/OxIkTuXjxosIM/wBHh2KOHDmIiIhg8uTJ9O7dG19fX+M9x/AToaGhrFmzBi8vL1asWEGtWrUUaHgGiX/PGzduJEuWLISEhBih2Gc5J8jTefh45LhWrVKlClWqVCE0NJR58+bx9ddfs337dmrXrk2vXr2oU6cOO3bs4I033qB8+fLYbDY8PDzo06fPn05b/nD//v0kVUUcYYZRo0bxyy+/cP36dWrVqkVISAgWi8W4dnWs00aNGtGwYUMaNWrEqlWrOHToEBcvXmThwoXUq1ePVKlSJctyicjLT4EGkf8Au93OtWvXyJAhA1u2bMHd3Z1OnTrx2muvJfesiYhIMvl/5S3z5cuH2Wxm5cqVnDlzhs2bN1OmTBk++ugjgoODgQdhhRQpUuDp6WkEIOTvi4qK4sCBAwQEBBidKpD0qZ/evXtjNptp3bo11atXp23btsk4x68Wi8WCn58f3t7ezJ49G5PJRLdu3YA/Qg0fffQR27ZtMzrTHw41vP3224806sjTuXbtGqdOnaJVq1YEBQUZr0+fPp1z584xc+ZMNmzYwPz58/noo48YPXo0r7/+OgDvv/8+JUuWNI5V8mQSd6TPmzePb775hkOHDlGqVClKly5tPMmWKlUqFixYwLvvvvtIqOHUqVNkyZIFd3f3R3732g+ejGM9jR8/nvDwcHLnzk2nTp1ImTIlhw8f5vvvv2fdunVcvHiRsWPHkiVLliShhlGjRtGzZ08KFiyoJ9+eQkJCAnFxcbz//vt8+eWXLFmyhHXr1rFhwwZ8fX0pXbo006ZNY/369fzyyy989dVXj1wXeXl54evri4+PT5KHB+x2OwUKFGDUqFFG5QeRF1XiYNvatWvZv38/R44coVq1atSqVYscOXKQI0cO3n33XeBB6f3p06cDPDbUkDjIoGuiJ/PwenIUVnbct3Xs2JF9+/axbds2cuXKRaNGjfDy8jLCDAC//vorsbGx9O/fn5QpUxpVBOTpOLbDyJEjmTNnDl27diUkJIRSpUo98zlBnlzi49GRI0f47bffOHHiBH5+fuTMmZPy5ctTrVo1goKC+PHHH5k0aRILFixg3759VK5cmbt37zJv3jzKly+Pi4vLI/uWjkePd+XKFZYvX07t2rXJkSOH8Xp0dDQXLlwgIiICi8XCTz/9BDwIbjqOU4mHPjOZTJQqVYoCBQoQHR3NiBEj2LBhA+vWreO9997TcCsi8kx05Bb5DzCZTJQtW5Z+/fqRLVs2Vq1aRVhYGL///ntyz5qIiDxHNpsNeNA44Lh5TEhIIDIyksjISONzwcHBfPDBB9y6dYvNmzeTP39+evToQd68eY2bVUdDWrZs2fD19X3uy/KqslqtWK1WIiMjuXbtGvDocB+5c+fm559/5pdffmH79u3Ex8cn81y/OpydnalTpw4DBw7E09OT6dOnM27cOOP94OBgJkyYQNasWY1QQ1xcHM7OziQkJAAk6URUQ9mzuXXrFhaLhbt37xqvzZs3jwULFvDOO+8QFBTEe++9R4ECBYiJieHDDz9k6dKlnD9/HsAIQTiOefLXHL/VsWPHMmzYMA4fPozJZOK7775jwoQJSfYDHx8fFixYQEBAAMeOHeP9999n+fLltG3blo4dOyapKCBPb/v27cycOZOiRYsyZswY3n33XerUqUPfvn0ZP348RYoU4ciRI3z66adcv36dLFmy8PHHH1OpUiWOHDnCxx9/zM2bN5N7MV4qbdq0oWHDhsTFxVG3bl3mz59P3rx5jfWYN29e6tWrx48//shXX31F4cKF6d69O3nz5jWOM3v37mXv3r34+/uTMmVKY9qO6y1HmEGjvsqLKnGwbcKECfTs2ZPly5dz6tQpwsLCGDduHIcOHQIeVGp49913adiwIefPn2f69OmsW7cOq9WKk5PTY8+/uib6a471Bw+GP5s5cyY9evSgT58+bNq0yejMdYQMZ82axYwZM7h9+7YRZjh48CBr164lICCAUqVKUb58eUDXRE/DarUaf+/evds4JxQuXBh4EP5/1nOCPJnEx6OwsDBat25Nv379mDt3LqNGjaJdu3a0bduWW7dukTVrVmrWrMny5cvp1KkT8KDCpMlkYs+ePcY1rI5BT+bcuXOsWLGCBQsWGJU49+7di7OzMz169KBBgwakSJGC77//nkWLFhnBhIfDVw4eHh74+fkxYMAAfHx82Lt372M/JyLyJFShQeQ/ws3NjXLlymGz2Zg6dSpffvkldrudTp06kTFjxuSePRER+RdduXKFDBky4OTklGRs6EWLFrFz504OHz6Mm5sbpUqVolq1apQpU4YGDRpw/vx5NmzYwN27dzlz5gyBgYHY7XZ27drFF198wd27d2nevDmZMmVK5iV8+fzZEwlp0qShcOHC7N69m/Pnz7Ns2bJHSpgC5M6dO8nwHvL0/qwkvpubG1WrVsVutzN06FDjycPElRomTJhgVGro3LkzkyZN0vb4mxI3gmXLlo2QkBDu378PPGjUnzZtGkWLFuXNN9/Ew8ODDBkykCJFCuBBMOuTTz5h+vTpZM2a1di31HD51xIfi7Zv386cOXMoU6YMH374Ia6uruzZs4dp06Yxbdo0rFYrPXv2BP4INbRs2ZKjR4/y448/YrPZePfddxVy+5t+/vlnLBYLLVu2JHfu3Njtdux2O66urgQGBjJ8+HC6devG7t272bx5M++88w6ZM2emX79+3LlzhwoVKpA+ffrkXoyXhqPCzuDBg9m3bx+XL1/GbDZTr149/P39sdlseHp6UqdOHS5evMiBAwdwcXExhrVxcnJiz549fP755yQkJNCkSROjHPnjqAFfXlQPV4hxBJztdjtz585ly5YtwIPrpaJFixqhBnhQqWHWrFnY7XbeeOMNVYh5Bok7cCdOnMisWbOIi4sz3l+/fj05c+akR48evPPOO8TFxTF79mymTp3Khg0bKFmyJAkJCezYsYPIyEgGDhyY5Fika6In59gO3377LSdPngSgf//+FCxYEOAfPSfI4zl+rxMnTmTKlCnkzZuXd999Fw8PD37//Xfmzp3Lrl27aN++PZ9//jnp0qXD29ubdu3a8fbbb7N48WKWLFlCVFQUmTNnTualefmYTCZWrlyJu7s7165dY8eOHQwdOpSqVavSpEkTrFYrq1atYvHixaROnZrq1asboYaHr3NMJhPx8fF4e3uTPXt2du/ezfHjx8mTJ4+uiUTkqSnQIPIf4CjT5erqSr58+XjzzTe5ePEi33zzDQCdO3fW8BMiIq+o/fv307ZtW+MpBkeYYcyYMcyYMQNPT08yZ87M1atXWbNmDd9++y116tSha9eudOzYEWdnZ9asWUPXrl354osvsFgsnD17FoC+fftSt25d4P8PYSFJJS6fef36dWJiYoiLiyNTpkx4e3tTqlQpNm/eTPv27YmOjqZChQp07NgxSQn9o0ePEhsbS7FixVTC+ik5npxyNJSdP3+ezJkzJymR6ebmRrVq1bDb7QwbNowZM2YASUMNEydOpEOHDuzatYvvvvuOihUrJs8CvaQeDpQkPn7kypWLjz76iJw5cwKwbds2oqKiaNeuHQEBAUbnrrOzM8HBwbzzzju4u7tTtmzZ574cL7PE28But3Ps2DFcXFzo1q2bcbzJkiULadKkYfjw4cycORMgSahh9uzZjBo1CpPJRNGiRalXr54xPZ0T/lri8wE86Fz/4YcfcHZ2Nsr8Ju7kggfbpFmzZvTr149t27bRtGlTADJlysSUKVPw8vICtA2elKNU8t69ezl37hxp06blxo0b9OvXj4wZM1KoUCEASpUqxf379zGbzXz//fccPHiQggULYrFYOHbsGFarlb59+1K7dm1A619eThs2bGDu3LmUKVOGHj16EBgYCMDt27f58ccf2bJlC/Hx8Tg5OVG4cGEj1ODk5MS8efMYN24c+fLlw9/fP5mX5OXjOB+HhYUxZcoUChYsSPPmzcmaNStHjx5l7969bN68mQ8//JBJkybRokULgoKCCA8PJyIigqVLl+Lu7k7mzJnp2rUrDRo0AHQselZr166lZ8+epEuXjowZMxrVvxwPB+ic8O/buXMns2bNomDBggwePJjcuXMb79WoUYMuXbpw5MgRunXrxvz584EH5/QMGTLQtWtXihcvjs1m0/3BUypQoAC9evUiPDycuXPnYrFYKFasGPnz5wcgICCA5s2bY7fbWbVqFdOmTQP4v6GGxO0Vnp6eeHl5aX8QkWeiQIPIKy5xA9iUKVPYuXMnx48fJyEhAavVyoYNGzCZTHTu3FnjuomIvGJsNhs///wz9+/fZ/r06bi6utKyZUsiIiKYMWOG0VGeN29efvvtN7755huWLFli3Lj27t2bgQMHUrZsWWbNmkVMTAzx8fHUrVuXqlWrUqlSJePf0VM/T+bhcerXrVvHmTNniI2NpXTp0nTq1IlGjRqxa9cutm/fTooUKXjjjTfIly+fMY0DBw4wc+ZMvLy8KF68eHItykvlp59+4ty5c9SoUSPJb3X06NHs2rWLfv36UaJEiUdCDdWrVzdCDY4nD7t37w48GNZg4sSJ/PrrrwozPKXEnbgRERFcunSJEydOUKhQIbJkyUJQUJDR+Hjz5k3WrFlDcHAwZcqUMb67c+dOvvvuO9555x2j0R50PHoajvU0YsQIzp8/j7OzM2XKlCE4ONgYi9vT09PYb4YOHfrYUMNnn30G/BFK0TZ4Mna73dgPzpw5Q44cOYx15+gQyZkz5yNPOptMJgoXLkyqVKk4deoUkZGRpE6dGkBhhmdkMpnIkiUL48ePp0CBAkybNo0lS5bQvHlz5s2bZzyVW7FiRTJmzMiePXuYPXs2x48fx2QyUaFCBerUqUO1atUA7QPy8vruu++w2+20b9/eCDPYbDZ27dpFypQpyZUrFzt37sRsNvP+++8blRoaN27M3bt3CQoKUpjhbzh06BDz5s0jKCgoSQduSEgINWvWJFu2bEyfPp0BAwYwduxYSpQoQYkSJfjxxx+JjIwkffr0eHp6kiVLFkDHor8je/bsvPHGG2zdupXr168za9Ys2rdvj6urq1HZR+eEf9eJEydISEhIUrHKZDJhsVjw8/Mzgj0RERFG9Tz4Y32HhoYa09I2eDJ2ux13d3dq1qzJypUrOXHihBESSRxK8Pf3N4a+cYQaTCYT1apVe2yowWKx0KtXL3744Qdy586Nh4fH8140EXlFKNAg8opzXLB98cUXfP755xQoUIAePXqQPn16du/ezf79+1m5ciWAQg0iIq8YJycn3n77bZycnBg1ahQTJ07E1dUVT09P3N3d6dSpkzHWp7+/P6+99hp58uThs88+Y/ny5fj7+9OoUSPeeOMNXn/9dVxdXYmPjydFihTG+UWNA0/n4XK+qVOnplSpUly8eJGoqChSpkyJk5MTn3/+uRE+GTVqFMePHydz5szcvHmTJUuWcO3aNQYMGKAnTp7AuXPnaNy4sTFMgWP822vXrnHs2DFOnTrF1KlTAR4baqhcuTJXr17liy++YPny5QBGqCF//vzG0yraF55M4k7cSZMmMXv2bGNoidmzZ+Pl5UX37t158803cXd3N7ZDZGSk0XF74MABpk6dipeXF+XKlUsyfW2Dp3Pjxg1++uknIiIigAfjQjvCDA9XLAGMUIPZbDYqlkDSChvaBk/Gsc7Gjh3L9OnT2bJlC1myZKFKlSp8++237N27l4oVK+Lt7W18x7FNMmTIgLe3N7GxsY+t0qMww//3uON1lixZyJQpE05OTgwaNIj4+Hi+/PJLmjVrZoQa7HY7gYGBBAYG8uabbwIPtom3t7exHXQueDIPr6fEww4pkPN8PLwNYmJiiIiIIGvWrBQpUsR4fdKkSezcuZMvvviCNGnS0KNHD7Zt2wZAXFwcoaGh5MyZk969e5MyZUpAoapndfbsWe7cuUPPnj2NMIMjyOnj40Pnzp25e/cuixYtYv369eTLlw9XV1fy5s37yLTsdruORU/ocb/XfPny0bx5c8xmM+vWreOrr77C39+fqlWr4uzsbIQaHOeE+vXrG0NE6Zzw9B43FOC+ffuw2Wz4+fkZr5nNZpydnbFaraRPn57hw4fTsmVLDh8+zP379/Hw8Hjs+tY2eDImkwmr1cr58+eJiIggKCiIu3fvsnnzZnx8fGjWrBlZs2YFHg01hIeHYzKZqFq16iP70/379ylQoAC///47n332GWnSpHneiyYirwgFGkReIX92oXz06FEWLFhArly5GDJkiHFjVKFCBX766SfGjh3LypUrsdvtGn5CROQV4+XlRf369bHZbIwZM4YxY8aQLl06fH19CQoKStKAkyJFCkqVKkWXLl0YNGgQGzZsoGHDhsZ7Tk5OuLq6GiX7QY0Dz2L9+vVMnz6dsmXL0q1bN4KCgoiMjMTV1RUvLy+jgWzOnDkMGzaMTZs2MX36dOP7mTJlYsiQIcZT6Woo+/8sFguvv/46ZrM5SanS9OnT07dvX6ZMmcLmzZuxWq3Ao6EGLy8vypQpw+TJk4mJiWH69OnExsbSv3//JP+OtsGTcRxvZs2axeTJkwkJCaFhw4bcv3+fs2fPsmTJEj799FMuXbpEu3btSJ06NXny5GHv3r00adKE4OBg9uzZQ1RUFAMHDnwk0CBPJ23atPTv35+pU6eya9cuLl68yMaNG6lRo4ZRiv/hUMPIkSOZNm0aMTExDBw4UJ1Wf1N0dDQAy5cvp1OnTgQGBpIjRw5Wr15Njhw5aNWqFc7OSZtuvvvuO86fP0/9+vXx8vJS5+FTSFwhZtu2bfz666+cOXOGKlWqULhwYXx9fQEYNmyYMYZ0s2bNmDNnjhGIi4uLM6piwB+d8epAfDKJt8GWLVv44Ycf+PXXX8mUKRPt2rUjXbp0yTyHr77E145r1qwhd+7cBAYG4uLiwvnz5zl69Cj58+dn1apVzJgxgzfeeIP8+fOTNm1aWrRowZAhQ9i2bRunT5+maNGiDB061Ahf6Xj07M6cOQNgHF8eHnLIxcWFevXqsXXrVr7++uv/u79oGzyZxMejy5cvExUVxa1bt8ifPz958+aldevW2Gw2vv76a2bNmoWLiwuvv/46zs7Oxn5kt9tJlSqVMU2dE57Ow8ejgIAAgoKCjN/2qVOnKFiwYJJ9wfF3hgwZ8PX15fjx49y4ccOoTiJPx3HcdgTPc+TIwaJFi3B3d+fMmTNMnDiRZcuWYbfbadGihbGeE4ca1q5dy+eff05cXBxvvPFGkul7e3vTvHlzGjdujJub2/NePBF5hSjQIPKKOHr0KL/++itVqlRJ8hQPPHgCMSoqihYtWiRJebu6ulKoUCE+/fRT+vfvz+rVqwHo2LEjmTJlet6LICIi/xIvLy/efvttAMaMGcP58+fJnDkzd+/eJWXKlNhsNqPRy9XVldKlS5MnTx4iIiL4/vvvKVOmTJLGGDXMPBtHQ8H333+PzWajbdu2xnisiTtGnJ2dOX78OBaLhQEDBtCsWTN++OEHoqKiyJEjB5kyZUoytrq2x+PFxMTg5eWFv78//fv3x8fHB3d3d+bOnYunpyf169cnKCiI9u3bY7fb2bJlC1988QXwR6jB8aR6SEgIwcHB5MuXj2XLlin8+QwSNxjHx8ezfv16AgICGDlyJLly5TI+V7BgQcaOHcuMGTPw9PSkffv2DB8+nA8//JCjR49y4cIF/Pz86N69uxG40n7wZP6skykwMJA2bdpgs9nYsmULixYtIm3atI+tWFKtWjVsNht9+/Y1ntCSZ+P43fbu3ZsjR46wadMm3nnnHUJCQnj//fcZOHAg48eP5969e9SoUYPAwEBMJhMRERFGVZnXX38dUMfVk0rcORgWFsbUqVOx2WzYbDZ2795N7dq1ad26tfHk4NChQwFYuXIlLVq0YMGCBdy9e5fFixfTuHFjSpcuDfyx/rUd/lribTBhwgRmzJiBxWIBHgQ233jjDdKlS6fj+r/MsW5HjRrFrFmzaN26NYGBgbz//vtERESQOXNmzp49y8yZM8mUKRPvvvsuadOmBf64Zs2fPz8//fQT9erVA7Qf/BMcHX0HDx6kQoUKjx1yKG/evOTMmZPvv/+eO3fuKAD0NyQ+Hs2aNYsVK1Zw7tw5rFYrgYGBdOvWjXLlytGyZUvsdjtff/014eHhwIPzb+JrpMS0Lzydxx2PgoKCKFmyJKtXr2bNmjUULFiQgIAA4zuO9e7n52dUOHQMuyVPJ/E9WkJCAgkJCXh4eBiVX7Jly8a9e/eYPn26Ua2wefPmSSo1tGrVioSEBFavXv1/f/cKM4jI36VAg8gr4MKFCzRv3pz79++TMWNGSpYsmeT9S5cuYbPZjJSwo3HeIXv27LRs2ZIBAwbwzTff4OTkRPv27RVqEBF5hXh5efHWW29hMpkYM2YMFy9eZP78+XTs2BEnJyej4dhqtRrDIBw8eJC4uLjknvWXWuIGeZPJRHR0NIcPHyZt2rTGUAUPN4TFxsaydOlS1q9fz+TJkylSpAjZsmV7ZNp66ufPff/99yxYsICWLVtSpEgRo1Tp1q1bGT58OAEBAXh4eFCjRg2CgoLo0KEDgBFqsNvtFC9e3Lhe+uabbzh8+DB9+vShZ8+eSa6j5Mk4GsrCw8MJDAwkNjaWatWqkStXLqM6htlspl69eri4uNC7d28mTpxI/vz5CQ0NZcGCBezYsYM0adLg6+trjNGtTq8nk7ix0mazcfv2bRISEkifPj0mk4ng4GDatm37p+GexKGG6tWrkz9/fnLmzJmci/TSSbwNACM05e7uzltvvcXQoUP54osv+Oyzz6hfvz5Wq5UxY8YQHh7Ohg0bCAgIwNnZmT179hATE0OfPn2MQIP8tcTnzHHjxjFt2jQyZsxI48aNuXjxIrt372bJkiXExcXRoUMHo/M2caihSZMmeHh4EB0dTYUKFZJrUV5qjm0QHh7O1KlTKVq0KC1atCBt2rTcuXPHCLjpuP7vSHwc2r9/PwsWLKBAgQJUrFgRgFq1alG5cmW8vb3Zu3cvv/76K0OHDjWuWeFB+5PZbKZ///5kzZrVeDJdlRmezP+7bqlYsSKLFy9m7969nD171rjWgT+e+ndycsLFxQV3d3c8PT2fyzy/qhzbYcKECUydOpWMGTPSpEkTTp8+jclkMgLkQUFBtGrVCuCRUIOGyXl2jzse5c+f3zgelS5dmvLly7Nnzx5WrFhB06ZNHwnTfvvtt5w6dYratWurYtUzSBzqWb58Od9++y1Xr17Fz8+PHj16kDFjRlxcXKhVqxYmk4lp06YZoQZHpYZLly6RPn16PvroI+rUqWOEPUVE/g0KNIi8Ary9valevTrx8fFGgjI+Pt4Ys83R2Lhnzx7atm2Li4tLkpsos9lMsWLF8Pb25tKlS6xYsYK7d+/y2Wef6QZJXjqJS6U5bmQcf6vT4/nTDWXy+LPfure3N3Xr1sXJyYmRI0eyePFismXLRu3atXFyckpy7rh79y5AkvKZ8nQSN9KcOnWKgIAAzGYzLi4u3L17lwsXLuDv7//IfuLu7k62bNmIiori9OnTScYxTkz71uNFR0czefJk9u/fb6xvR0N81qxZadiwIWvWrGHatGnY7XZq1qz5SKghLCyMd955h9q1a7Nv3z5mzZpF+vTpcXd3N8IMOqc8vd27dzN+/Hjc3NyIi4ujRo0awB9hB8e+UKtWLa5evcqoUaPYsWMHxYsXx9XVlapVqyaZnkI9TybxsWjlypXs2rWLQ4cOYbFYKFiwIAUKFKB169aEhITQrl07gMeGGhy/eXd3d+P+QvvBk3Nsg9WrV+Pl5UXlypWN40mlSpVYvHgxa9asoUqVKpQvX54GDRqQLl06Nm7cyIYNGzh//jwmk4lChQrRuHFj6tSpA2gbPCnHOXPlypXMnDmT8uXL06VLF4KDg4mOjsbb25uZM2fy9ddfAw+qFiau1ODq6sqWLVswm8107dqV+vXrJ9uyvOwOHDjArFmzKFy4MB9//DGBgYHGe5cuXeLgwYNERERQpkwZgoODSZkyZTLO7avFcRzavn07R48exW6306tXL2M4FVdXV1xcXLDb7WzatAl4MESXw/79+1m8eDGBgYFkzJjRuE/QcejJJD4fHz9+nMjISFKkSEGuXLnw8vIiXbp0FClShM2bNzNy5EiGDBliBA/hwXFs//79HDp0iJIlS+Lr66v77b9p69atzJgxg9DQUHr27ElgYCDR0dHGsHOO9RsYGEi7du2w2Wxs2LCBGTNmYLPZqFSpktb/M3rc8ah3797G8Sh9+vTUr1+fCxcusHDhQu7evcsbb7xBiRIljIpV06ZNw8nJiSpVqihw/gwcx+2xY8caQ1w6OTnxww8/8Ntvv9G7d29KlCiBq6srNWvWBDAqNdjtdoKCgvjmm2/w9fVl0KBBRphB5wQR+bco0CDykrPb7fj6+jJgwADMZjNubm5MmzYNT09P6tati5eXFwUKFMDf35+IiAg+//xzOnbsiNlsxmq1GhcYqVOnJmPGjOTKlYvr16+TP39+hRnkpZO4geDWrVtER0djsVhIkSIFr732mi6on4OHb1wS39yrseX5SLwf7Ny5k4sXL+Lq6kqePHnInz8/vr6+1KlTx3jyc/LkyVgsFurVq2eEGQ4fPszGjRvJkCGDAg1/g2M7jB07lgULFjB9+nSKFi1KsWLF+Pnnn9m6dSv+/v5JOgod+4njCcXTp08DahR4Gt7e3rRv3x6z2cymTZuwWq20adOG/Pnzkzt3blq0aIGTkxMrVqwwGm4coQZHxRJHRYYJEyZw6dIlAPr370+ePHmMf0fb4+mVKVOG1q1bs2rVKuLi4jh58qTRkQgkCR/WrFmTL774gp9//hln58fftuqc8tccY+ECjB49mpkzZ+Lu7k6OHDm4efMm33//Pdu3byciIoIpU6b8ZajhYdoPns6MGTMYM2YMadOm5dChQ3Tq1AlXV1cyZsxIjx496NSpE9u3b6d8+fIAVKhQgbJly/Lhhx9y69YtvLy8SJUqldHBq3PD04mPj2fjxo2kSpWKrl27Gh3pbm5uHD16FF9fX7y8vFi2bBlAkkoNn3zyCa1atcLJyYmMGTMCWv/P6syZM0RFRdG4cWMCAwOx2+3Y7XaWL1/OkiVLOHHiBDabjblz59KhQwfatm2LyWTSMf8fsnHjRj766CMyZMhApkyZjDLuFosFZ2dnYz2HhISwefNmduzYQcqUKbl16xaTJ08mMjKS7t27JxnqQPvBX0t8Pg4LC2Pu3LlER0eTMmVKihQpwqBBg/Dz86Nz586cO3eOXbt20bt3b959910KFSpEmjRp2LNnD5MnT+bu3bvUrVsXd3f3ZF6ql5fjnuvgwYNYLBbatm1LYGAgNpstyTC+JpOJEydOcOnSJUJDQ2nXrh0uLi6sXr2aqKgocufOTZYsWZJxSV5uf3Y8cjxoUa1aNeLi4pg7dy4rVqxg1apVlCxZkvj4eI4cOUJCQgJ9+vShcuXKybwkL5fEbXOrVq1i1qxZlCpVinfffZd06dIxceJE9uzZw2effUa/fv0oWbIkbm5u1KxZE5PJxMyZM1m0aJExvQEDBiQ5HumcICL/FgUaRF5yjguQFClSAA86r8aNG0eGDBlIkSIFFStWxMfHhz59+tCjRw8WLlyIh4cH77//fpKSp/v27ePo0aN06NCBZs2a4eHhAagDUl4eiTtxFyxYwLp16zh16hQ2m41UqVJRtWpVWrVqRZo0aXRx/S9JvA1Onz7N9evXuXTpEgEBAfj5+eHn56djyr8scUPZpEmTmDx5svFezpw5admyJQ0aNMDHx4f69etjt9sZO3YsH3/8MREREeTLl49bt26xZs0afv/9dwYNGmSU2pQnl3hfWL58OdOnTyc4OBibzQZAuXLlmDt3LuPHjyddunTUr1/fCDU49o/IyEgAY/3ruPVkHMeYUqVK4ezsjMViYevWrdjtdj744AMKFixIzpw5ee+99wAeCTUEBgbSuXNncubMyVdffUVkZCQhISG89957xhjROo49mYcrJjn+26NHDwAWLlzId999x5EjRyhbtuwj6zVdunS4ubmRkJCAxWLRU1fPyLFO586dazyV3q5dOwoVKsSFCxc4deoUw4YNMyq5TZs2jZCQEKNiybZt25g4cSIdOnSgXLlyybkorwTH8Co3btxg1qxZHDp0iLfffpsqVapQsWJFKleuzJIlS6hWrRqlSpUCHmzDjBkzGp3oDqpQ8vRu3LjB7t27qVSpktGRbjKZmDBhAocPH2bs2LGYTCY+/fRT1q5dS3x8PF26dDE6bjNnzmxMS+v/2VksFgBcXFyIi4vjm2++YcuWLWzYsAGAatWq4eLiwt69e/niiy+oWLFikkCh/D25cuWiYcOGrFy5EqvVysKFC2nfvj3Ozs5JzsX58+fH39+fpUuXsnjxYuBBiKdv377Url0b0DXR03Csp1mzZhEWFkaWLFkoWLAg58+fZ/v27URGRjJx4kQCAgIYOXIkgwcP5vvvv+eHH34gffr0eHl5cfLkSaMD11HlStvg2djtduLj49m3bx+urq5kz54dePSeKz4+nq1btzJ79mzCwsIoWbIk7777LtHR0ZQoUUJhhr/pz45Hrq6uxnDJderUIXPmzGzfvp358+cTERGB2WymaNGivP3229SqVQtQyPBJPbyefvvtN1KlSkWPHj0ICQkBYOLEiQwdOpRVq1YxbNiwJKGGGjVq4O3tzfr16/n999958803japVOh6JyL/NZHcMwiUir4Rbt24xbdo0li1bho+PDx07dqR69eq4uLiwePFiwsLCuH//PrVr16Zdu3Z4eHhw/PhxwsPDOXr0KNOmTTNKROlCRF4WiX+rY8aMYcaMGfj4+FC8eHESEhLYu3cvcXFxFC9enA4dOlCsWLEkgR75+xLfFE2bNo0lS5Zw+fJl4EFjZcaMGenXr5/xxKH8u5YtW8bAgQMJCAigTJkynDlzhh07duDt7U3Xrl1p0qQJAHfu3GHVqlWMGTOGhIQEvL29MZvN5MmTh5o1a9KoUSNA54Nn9fPPP7N3714WLlzIzJkzk4RDFi5cyJAhQwAYNGgQjRs3Nt47ePAggwcP5vz588ycOdMouylPxtH4BQ/W5YQJE4iIiKB69eo0b96cQoUKAQ+CV/Pnz2fFihX4+/vzwQcfULNmTWP4lbi4OO7cuYOHhwepU6cG1FD2pBKHeq5fv47dbsfb29sIzMIfY9inSZOGyZMnU6BAgSTT2L17N61bt6Zp06YMGDBA6/5vuH79Oq1bt+bmzZvMnDnzkY7BK1eu0KxZM86fP0+DBg0YNGgQZrOZEydOMH78eHbs2MGECROoXr16Mi3Byy/x7/fDDz9k8+bN1KtXj6NHj/Lbb79Rrlw5Bg0axC+//EKXLl0IDQ1l8ODB+Pn5JfOcv1pu3bpFzZo1yZ49u9FB67hmatSoEb169TKCV9u2bcPHxwd/f38++ugjihcvnsxz/3JLvA/s3LmTtm3b4uTkRKZMmbhw4QLOzs7kypWL9u3bU61aNeCPEtjDhg3TEB//gIfD54sXL2bBggWkSpWKjz/+2OgUTPy5r7/+ml27dvHdd99RpEgRqlevbpwLdF5+OjabjXv37tG6dWtMJhNDhw4lZ86cHD9+nEGDBnHkyBHy58/PpEmTyJAhA+fOnWPTpk188803nD59GmdnZ0qWLEmNGjWMfUTb4O/r2LEj3377LStXriQgIMCoVOJgt9tZsWIFAwYMoG3btnTt2hV4ED533B/oXvnpPenx6OHtcenSJWw2G87Oznh7e+Pl5QVoX3gWo0aN4tatWxw9epQiRYowePBg4I976ejoaIYNG8aqVavIli1bklCD4zd/584dVQ0TkedKFRpEXiGOJ9HbtWuHyWRi0aJFfPHFF8ZYxA0bNiRlypSMHz+e1atXs379eux2OwkJCQD07dvXCDOAyvjKyyNxqbQZM2ZQpkwZevToYZSR/f3332nUqBH79+8nJCSEfPnyaUiVf5jjxmX8+PGEh4eTPXt2unbtyr179zhz5gybN2+mbdu2DBgwgPr16yfp1JK/z3FD6biJjIiIIEeOHIwdO5bcuXNz9epVvvzySyZOnMj48eMBaNKkCSlTpqRu3bo4OTkxduxYPDw8aNGiBe+++64x/IRuTJ/NyJEjmT17Nq+99hoZMmQwwgyORpmmTZty9+5dxo0bx6BBg9i/fz9ZsmTB2dmZVatWcfnyZfr3768ww1Oy2WxGmGHdunWcPXuWS5cuYTKZ2LhxI05OTjg5ORnDcT1cqcFkMlGzZk1cXV1xdXXF29sbR/5bT+M+GZvNZjRQzps3j9WrV3Pz5k3Spk3LgAEDCAwMxN3dnW7dumEymQgPD6ddu3b07NmTfPnyERAQwLfffkt4eDjwYJgKUJWSv+PatWucOHGCevXqkSdPHmPYOZPJhNVqJUOGDEyaNInWrVuzZ88efv31V/LkyUOePHno0qULjRs3pkKFCsm9GC+VxA31gBGUcnV1pVGjRhw+fJgrV64wefJkRowYwY4dO3jrrbfo1KkTwcHBHD16lNOnT+Pn5/fItOSv/dm1i9lsplixYty9e5fo6GhOnjzJ9OnTCQoKolGjRsb9QXBwMNu2bcPZ2ZlDhw5x6tQpBRqe0sPbIPHf5cuX58MPP+Srr77i1q1bBAQE0KxZMwoXLoy/v7/xXUcVyoerk8iTeXgbJD6O+Pv707BhQywWC0uWLGHq1Km4uLhQtWpVzGaz0aFVq1YtatWqRUxMDJ6ensZ9t+4Pnkzi9eTk5ERcXBzXrl2ja9eu5MyZE7vdTmBgIKNHj6Znz54cOXKEDz/8kEmTJpEtWzZatWpF69atuXHjBs7Ozvj4+BjbUdvgyT1cLQwedNqaTCZy5MjBN998w9SpUxk7dizOzs7Gedexjh33Y3fu3DGmqTDD03nW41Hi7QGQKVMm43u6R3t2N2/eZOXKldy9excvLy+jgpijKp5j6JV+/foBGJUa+vfvT8mSJY22IsfwLNoGIvK8KNAg8gpxjL3t4+NDmzZtAFi0aBGff/45ADVq1ODNN9+kdOnSzJ07l99//53ff/+dQoUKUbJkSePJad0Y/bmH08HyYnAEc3bs2IGLiwsdOnQwwgwA69ev59q1a5QtW5YmTZoQFxeHxWLBx8dHN6D/oK1btzJ9+nSKFStG//79k2yD9957j4iICL799ltq1aqlQMM/KPENvs1mIz4+nuPHj1OrVi1y584NgJ+fn1GV4eFQg6+vL3Xq1CEuLs4Y19txg6ob02fneAL6999/J2XKlJw7d45s2bIlWZ9t2rQhffr0hIeHs3nzZqMEc7Zs2RgyZAgNGjQAdF5+Go71lPjp/zx58pA7d2527NjB+vXruX//Pu3btzdKKScONcycORObzUbt2rWNc8PD/5X/z7ENJk6cyJQpU3ByciJVqlT89NNPdOnShd69e1O+fHk8PT2Np9ymT5/Op59+iru7O6+99hqnTp3C1dWVPn368Prrryfn4rwSHOFlx284cSOyo8He39+fihUrsmzZMn755RfjGBYUFERQUBCgY9GTSjz808KFC3F1daVBgwbGuTV//vyEhoaydu1afv31V6ZOncrChQv56quvGDp0KN7e3kRFRTFx4kSKFSum4Vae0sNPfV69epXo6GiyZ89Onjx5GDZsGLGxsXh7e3P06FEuXLjAuHHjCA4ONqbx+++/kz59elasWMH58+cpWrRoci3OSynxNjh69Cjnz5/np59+wt/fn/Tp01OuXDk6dOhA3bp1iYuLM8rpwx/XngcOHGDFihVkz56d1157LTkX56WRuK0i8d87d+7k9OnTHDp0iGLFipEtWzYqVKhA7ty5adasGTabjWXLlhntRlWrVsXFxSXJdnRsH9D9wZNKvP62bNnCpUuXuHHjhjGsHPxxXs6SJQtjxoyhR48eHDlyhC5dujBp0iRjyMb06dM/Mn1tgyeTeDskJCRw584d0qZNi5OTE2azmSZNmrB69Wq+/vprMmXKRLdu3ZKEegBOnjwJJO1Md9D9weP9k8cjs9n82HY73aM9uYfDsWnSpGHBggV069aNU6dOERERYVQdSRzoeTjUMGjQIPr160elSpUwmUzaBiLy3KlXTuQV40gd+/r6PjbUUKlSJfz8/OjevTtms/mRixo1VP65ffv2ceDAAd555x0jjS3P1/Xr13F2diZVqlRJbmhMJhMxMTEcO3aMgICAJE80h4WFERYWRmhoKN27d8disRjj7HXv3l0X3v8Ax7Y4dOgQNpuNjh07JgkzhIeHExERQfny5RkwYADR0dFcuHCBfPnyJeNcvxoSPwm9cuVKIiIicHd35/bt29y6dctI2pvNZnx8fGjatCnwaKjBx8eHBg0aUKpUKWPcRNCN6ZN63NOz9erVI0WKFHz44YecOHGCBQsW0L9/f5ycnJI8HV2vXj2KFCnC9evXOXXqFDlz5iR9+vRky5YN0Hn5WXz99ddMmzaN0NBQevXqZXTM7t27l2XLlrFx40bsdjsdOnRIEmpwcnJi0aJFjB8/nrx58yYZIkSeztmzZ1m9erXRaeXv78/w4cP58ssvGTZsGECSUIOj8TIqKor06dPTt2/f/7F33wE97f8fwJ+fdhqUKK5x3ei0lew9ssfFvVllZY+MrIxsrlmokCIi3Fx771wyKhGhlFVUVmlpfj6v3x/9Puf7+ShU6KT7fvxz8znnc+455/k5+3Xeb5iamvLHc7YdFM/nijRVVVUBFJzLRkdHF+pyQiQSQVlZmf88IyOjyOmxDL5O9nhw5coVvmuhmzdvYuLEidDX14eWlhYmTJiAixcvws/PD61bt4a9vT3atWuHEydOwNvbGyKRCBEREYiIiGAP00tA9rxo27Zt8Pf3x7t37/jhy5cvxx9//AFNTU1kZWXh0qVL0NDQQKtWrfhxbty4gTNnzsDKygpVqlSBnp4egKKP9Uxhshl4eXnB398fqampcuMMGTIEEydORM2aNfl9THh4OFRUVPDbb78hNDQUHh4eePXqFZYtW8afEzGf9+m9CunDQ3d3d/j4+EAikQAoKEAHCorNJ02ahN9++w0jR44EgCIfIhZ1/GXXB18nW9gmbcFQVkxMDD5+/Mi3QgIAtWrVkitqmD59Otzd3fmiBrbeS052v33w4EFcuHABYWFh+OWXX/junWrWrIkVK1Zg5syZ2LZtGzIzM7FgwQK+mCEsLAzbt2+HkpISLC0thVycn8aP2B/JtkbJlJx0O7h8+TJatWoFFRUVNGjQABs2bICzszOio6Ph5OSELVu2QFtbu8iihvz8fBw/fhzp6elsf1RC7BySYb4fVtDAMBXQ54oaPD09QUTo2rUrX2H/abNr7OSwaG/evMGUKVOQmpoKBQUFDBo0CDo6OkLP1n8GESEzMxObN2+GkZERunfvjipVqgD434mhtLm5nJwc/jf9aTGDsbExbt26hYSEBDx69EjAJapYpC1k3Lp1C+rq6nIPAWUzcHJygqamJmxtbVG3bl34+flBU1OTXQx9g0/fRpcVExOD9PR0VKlShX8rQltbW66oYdOmTSAi2Nvbo3LlyqhcuTIA9vCwpKQXp+vXr0eNGjX41jC6dOkCT09PTJ48Gbt374aGhgamTZvG3xwACo7DtWvXRu3atQt1L8HegCudO3fuQElJCaNHj+ab11dUVESLFi34t0APHDgAZWVljBo1ClZWVjA0NOS7AWHFDCX36T7j3bt3SExMxMqVK2FlZQUAWLx4MTQ0NLB79+5CRQ0zZsyARCLBvn378ObNG+jp6cHc3BwA2w6KSzYDiUSCDx8+oEqVKiAimJiYoGfPnjh9+jTOnz8PAwMDfn8v+9ZcSkoKgIKmfwH20Ko0pMeD5cuXQyQSYfv27Vi+fDlOnjyJiIgIdO7cGYMHD0bdunUxc+ZMLFq0CP7+/hg7dixq1aqF8ePHw9zcHIcPH4aNjQ0rZigh6TawadMmbN68GRzHYeTIkXj79i0SEhLQrFkzuZZKFBQUkJmZiUOHDmHkyJG4ceMGNm/eDAAYOHAg36qGdHzm6z7ths7Y2BgzZ84EUFCcvnXrVuzduxfJyclwcXGBgYEB4uLiMHbsWGRlZUFfXx+vX7+GWCyGi4sL31oVe6D7eZ+7VxEQEABvb2/Y2Nhg+PDhAAr6n9+4cSN2796NN2/eYP78+ahXrx6GDRsGoOAh4pYtWyCRSNCtWzd2/C0l6W9179698Pb2hqmpKdq0aYOoqChcuXIFu3fvhrGxMXr06CH3PWlRw5w5c3D79m04Ojrin3/+gZqamhCL8VOTLa5av349fHx8oKKiwl8bA//bX7Vs2RKurq5Yvnw5AgICEBkZCSMjIygrK+Ps2bN4//495s6dy7oeKga2Pyq/tmzZgo0bN8LBwQFz5syBsrIyDA0N4ebmhunTp+P27duYOnUqNm7cWGRRw4IFC9C3b1+5IlCmeGRbbrOwsGDFUQzzLYhhmApLIpEQEVFKSgqtXr2aGjZsSLa2thQYGEjp6ekCz93PZ/fu3dSqVSviOI48PDwoOTlZ6Fn6T8nLy6PevXtTy5Yt6eDBg0RUkMm5c+coJyeHsrKyyNHRkTiOo9u3b5Ofnx9xHEeOjo704MEDfjqPHj0iU1NT6tu3L+Xm5gq1OBWSk5MTWVlZ0evXr4mIaNOmTYUySE9Pp7Zt25KtrS1lZ2cLObs/Nen+nYjoxIkTZG5uTsOGDaN9+/bRrl27yMrKijiOo6lTp/Lj5eXl8X+npqbS1q1bieM44jiOoqKiynL2K6SbN2/y6/PQoUNyw86fP88P27BhA/+5WCyWG082V6Z0cnJyyM7OjmxsbOjVq1dERJSfny83zqNHj2jgwIHEcRw5OzvTnTt3+GEfPnzg/2Z5FI/s+o2MjKRbt25RQEAA/fnnn5SRkUFEBblI/7t8+XLiOI5atWpFJ0+e5MchIlq/fj1xHEdNmjShgwcPUmZmZtkuzE9KNoPAwEAaN24cWVlZUb9+/cjFxYVycnLo3Llz1KZNGzI3N6ft27dTYmKi3DRu375Ntra21KVLF3rx4kVZL0KFIZFIKDo6mjiOI3t7eyIievXqFXl7e1Pv3r2J4zhq3bo1nTp1ikJDQ2n06NHUrFkzCgkJkZuO7LXap8cK5suuXr1KlpaWZG9vT9HR0fznRe1Pzpw5Q9bW1sRxHHXr1o0/Vvv7+/PjsGNByV28eJHMzMzIwcGh0DlmaGgo9e3blziOo3nz5vGfr1q1igYNGkRt2rSh6dOn05kzZ/hhbBv4Otl7FZs2baI3b97Q2LFjqVu3bhQTEyM37ucyiI2NpcWLFxPHcdS+fXt2LCiFT885x40bR7169aLY2Fj+s2XLlhHHcWRlZUXnz58vcjpxcXHUo0cP8vT0/KHz+18gvS80duxYevDgAWVkZNDTp0/5c9O8vDx+HxMeHk79+/enJk2a8Bn16dOHv/9ExPZHxcH2R+VPfn4+HT16lFq2bEnGxsa0YsUKufuhsbGx1KtXL+I4jkaMGEGpqan892T/K8W2g5I7e/YscRxH//zzDxGxdcgwpcVaaGCYCuzTlhoUFBTg5+eH1atXw9raGvXr1xd6Fn8K0jfeHBwcoKysDDc3N3h6egIoaC6TdT9RNvLy8mBjY4Njx47B09MTJ06cwPXr1zF27Fi0bNkSGhoaaN++PYKDg+Hg4ACJRIL27dtj8uTJcn3iPn36FBKJBO3atWN9EpcC/f/bUSTzllReXh4UFRVhYGCArKwseHh4QE9PD1u2bOFbx5BmoKqqCpFIhKysLGRnZ/PNYDPF9+mb0PHx8dDQ0MDcuXP5rj4aN26MoUOH4syZM5gxYwbWr18PJSUluZYaBg0ahI8fP0JXV7dQ8+NMyTVr1oxvGnbevHkgIvTv3x8AYGtry7fUsGXLFgDA1KlT+e4npBX77M3Db6esrIxq1arh3r17iIyMRM2aNQu9VWtsbIw//vgDd+/excmTJ5GRkYFx48ahUaNG/FvrxN4ELRaSaVJ58+bN2LlzJ9LT0/lWk27cuAFbW1uoqKggLy8PKioqmDVrFgDwLTWIRCK0bdsWGhoacHZ2hkgkgo+PD9atW4ecnBz07dsX6urqgi1jeSebgfQNRC0tLdSuXRtJSUn48OEDMjMz0blzZyQkJMDT0xPu7u64d+8eOnbsiAYNGiA6Ohp+fn6Ij4/H8uXLUadOHYGX6uclEolgZGSE3r1749y5c7h16xaaNWuGESNGYODAgfDw8MCJEycwa9YsdOnSBWpqaiAinD59GkZGRtDW1oZIJOJb0yPWQkmJRUVFIScnB6NHj4aRkRF/nJVt2j0mJgaRkZGoVasWpkyZAh8fH6SkpMDa2hr29vbo1asXANZiVWk9ePAA+fn5GDNmDDiOAxHxx9XGjRtj4cKFcHJywsGDB8FxHIYNG4Y5c+ZALBbj48ePUFdX51uOYRl8WVH3Kry8vJCbm4uYmBh0794d9evXR35+PhQUFKCgoIDGjRvD1dUV06ZNw8GDB2FmZoYhQ4bA0NAQDg4OyMjIgLm5OTsWlIL0eLxu3Tr8+uuv+PDhA3r37g1DQ0Pk5ORAVVUVCxYsgKKiInbt2oVZs2Zh7dq1sLW15adBRKhduzb+/vtvuWMBOy8tuZiYGOzduxd16tTBlClT+HsS9erVw71793D//n1cuXIFBgYGGDJkCKytreHr64u3b9/i2bNnqFOnDrS0tFCrVi0AbH/0NWx/VH4pKiqiW7duUFNTw9KlS+Hv7w8AmDVrFt9Sw4YNGzBt2jTcuHEDU6dOxaZNm6ClpSXXmpsU2w5KTldXF+rq6ti5cyc6derEtzrMMEwJCVFFwTBM2ZJtqWHx4sW0Z88egefo5yOt3iYiOnz4MDVv3pw4jiMvLy96//69gHP23yD9DWdlZdHmzZvJ0tKSjI2NqV+/fnJv1hIRzZs3jziOo0aNGlFgYKDcsJCQEOrXrx9ZW1tTcHBwWc1+hSFblZ2amkopKSlyw5OSkqhdu3b8222Ojo705MkTuXGuXr1KHMfRjBkzKC8vj7319g0WL15MDg4ONHLkSJo/fz4RFVR5SyvtHz16RI0aNeLfQpeSbalBdt/GKsS/D29vb+I4joyNjeXe5iGSb6lh06ZNAs1hxbd//37iOI7GjRtHT58+lRsm/f1HRERQixYtyM7OjjiOowsXLggxqxWGl5cXcRxHzZs3Jzs7O2ratClxHEfDhw+n+/fv8+NJ90/SlhpMTEyoadOmdOTIEbn90YYNG4jjOOrQoQOlpaWV+fL8jAIDA/k3EB8+fEhEBef+L1++lBtv37599Mcff/D7ImNjY/4txN27d/PjsePztzl8+DBxHEcjR44s1DLerVu3+JZKpNtKs2bNKCwsTKC5rRjEYjFJJBIaPnw4cRxHd+/eJYlEUuj8Jicnh/bt20cmJiZ0+PBhIiJ68+YNJSQk0Lt37+Smx5SMWCwmsVjMH1sfPXrEf/apc+fO8dtIWloa2+d8A9n1+/fff/P7FY7jaNGiRZ/9nvS8dOLEiXIt50nfzCVix4LSiIiI4Nc/x3G0bt06fpjsddhff/311ZYaiFgG3yIiIoLMzMzI3d2diArOQ5OTk8nHx4eaNm1KpqamfE6tWrWSa0njUyyH4mH7I+F9esyV/XdOTg6dOXOGb0HjSy01ODg4yLVeyHy7qVOnEsdxdPToUSJi55oMUxqsnIph/gNEIhEkEgmqVKmCuXPn8v2nS/vvZr5MLBbz/afeuHGD7z8MKOifNTAwkO9zmPkxpL9hNTU1xMXFIScnB8rKynjz5g3i4uKQlpbGjzthwgT06NEDmZmZWLlyJbZt24bAwEBs27YNM2fOxMOHD+Hs7IyWLVsKuEQ/H9k+KAMCAjB+/HgMGDAA48ePx7Nnz5Cbmwt9fX1MmzaNb7WkWrVq+O233/hp3LhxA56enlBUVET37t2hpKTE3jQppYyMDNy4cQOhoaEICQnB06dP8eHDBygoKEBZWRlisRjGxsYICAiAhoYGTp48iRkzZgAAlJSUkJeXBwByfUOzKvviEYvFhT6TPZ6OHTsWzs7OICLMnz8fhw4d4odJW2oAAC8vL6xevfrHz3AF9On5S15eHt8iAAB07twZLVq0QHBwMA4ePIhXr17xw6T7sadPnyItLQ2DBw+Gv78/OnXqVDYzX0HIbgepqak4c+YMmjRpgp07dyIwMBBz586FtbU1QkJC4O/vj6ioKAAFLWjIttQwaNAgpKamIjc3FyoqKny2U6dOxZQpU7Bt2zb+nIsB/zv/dD+Ul5eHixcvQltbG1OmTIGJiQny8/NRpUoV/PLLL/x4ycnJ6NatGzZs2ABXV1d07doV7dq1w+zZs7FlyxY4ODgAKNjG2PH566S/V/r/t8+lfwPA77//DhsbG0RGRiI+Ph4AkJubCwBo2rQp5s+fD19fXzRt2hS6urr48OEDPx5TOgoKChCJRDAxMQEAvH37FiKRqNBvWUVFBfr6+pBIJLh06RJyc3NRrVo11KhRgz+HJdYyRqlI37j99ddfAQApKSn8Z1LSbcTa2hp6enqIi4tj+5xvpKCgwO+PBgwYgKlTp6JmzZoQiUSIjo5GQkKC3PjSfZaNjQ2qV6+O6OhoZGVl8cO1tbX58VguJWdpaYmFCxeiVq1aEIlEePDgAZ48eQIAfIt5AODi4oIRI0YgKysLc+fOxblz54qcHsug9D5+/Ij8/HzcuHEDd+/ehZ+fH5ydnbFu3TqkpqZiyJAhWLlyJbp06YJ3797h8uXLn50Wy6F42P5IWLLnL8+ePeNbw5Aee1VUVNChQwe4urpCT08P/v7+WLt2LX+OKm2poV69evy9JqZkZO9LSEm3idGjR0NLSwunT58GwO7BMUxpsC4nGOY/QtqsNXt4VTJURDO+lSpVQrVq1dCgQQPExMRgw4YNyM/Ph4ODA2sy6geS/l7r1auHJk2aoE6dOjh16hTWr1+P7OxsdO/enW8OcMGCBahevTp27twJNzc3fho1a9bE0qVLMWDAAACsycCSkK4nd3d3eHt7AyjoPiIuLg4JCQlwcXFBy5Yt0bFjR6Snp2Pr1q04cuQInj9/Do7jkJubizNnziArKwvz5s1jDw9L6NNm/jQ1NbFr1y5MmzYNd+7cwZs3bxAVFYXmzZsDKHhoK1vUYG9vj5MnTyI/Px8bN25k3a18A+kx4ciRI6hVqxYaN27M3ySQ3mQZO3YsAMDNzQ0LFiwAEeGPP/4AUFDU4OHhAScnJ2hoaAizED8x2S46Tp06hXv37iEsLAyamppo2bIlWrduDVNTUwwbNgwvX76Ev78/MjIy8Pvvv8Pa2hoikQjh4eHYs2cPatSogVatWqF69eoA2DGhJKQZXLx4EZmZmYiNjYW7uzvffU3fvn2hra2Nbdu24cSJEyAijBo1CsbGxlBWVkZ+fj5UVFTg4uKC7t27o0mTJgAg1w3LxIkTBVu+8ujGjRs4e/YsXFxcoKamJrctvHv3DsHBwWjUqBHMzMyKbBr29evXWLduHeLi4uDn5wd7e3vY29sX+t2z7aB4ZNfThw8foKOjA6DggYe0QGfAgAGYM2cO9u3bh6VLl0JFRUXuWNG6dWtwHIfY2Fi8e/cOvXv3Fmx5KpJ69eoBAH9D3tDQkB8mza1FixbQ1NREdna23LYizYY9NPk20gx8fHxQu3Ztvrl2WXp6elBVVYWqqmqh/RVTctKHiAoKChgyZAiAgq6g7ty5g0uXLvEFa8D/CtV1dHSgoaEBIioyA7YdlFxRGVy/fh1HjhzBiBEjULVqVbluAF1cXCASieDn54cpU6bg/PnzqF27tsBLUXE0b94c7du3R1BQEEaNGoXMzExoaWmhdevWGDZsGNq2bQugoDu6c+fO4dmzZwLPccXA9kfCka6ndevW4fz585g3bx5atWoFJSUl/hxUWtQgkUiwfPly7N69GxKJBLNnz4aKigoMDQ2xceNGREVFoXPnzgIv0c9F9vosOzsbampqctcMNWvWBMdxuHz5Ms6cOYNu3boJObsM81NiVw0MU059rvq0tDcZZd+uDg8Ph7m5uVxxA1P0Opf++8CBA/Dx8UH79u3l+t7bs2cPdu/eDU9PT0gkEgwbNowVNXxHRWUyduxYDB06FOrq6tDV1cWePXvg5eUFkUiErl27QltbG7q6unBxcUGHDh2QlJSEJ0+eoGHDhqhTpw4aNGgAgN2wL42oqCj8888/aNu2LcaOHQt9fX2sWLECQUFBWL58ORYsWICWLVvizz//RP369bF69Wo8fPgQd+/ehYaGBurXrw8HBwf8/vvvAFgGJSG9qD9+/Dj/wENfXx8bNmzA1KlTcffuXaxbtw5r1qzhW8X4tKjBzs4OZ8+exb///svfvGFK58CBA3B1dUWHDh2grKyMhg0bQiQSFSpqyM3NhaenJxYtWgSJRAI7OzsABS0IsBuWJSd7LrN+/Xr4+vryN8AiIyNx8+ZNHD16FAMGDMDw4cORnZ0NHx8f7N+/H0eOHEGrVq0gFotx+/ZtpKenY/78+XwxA8AKPUvq1KlTcHZ2RqNGjVCtWjX+gZX0YW7Hjh0hEong7e2NkydPAgBf1CBtKUZFRYUvZpAeE6QZM/+TkpKCuXPnIikpCYqKipg9ezZUVVX5m2YikQhKSkpIS0tDVlYW1NXVC02jWrVqSEpKQkREBCIiItCiRYsi30Jn20HxSNfTokWLcOzYMcyYMQOWlpawtLTkr7EaNWqEmjVr4syZM/jjjz/4Y4WsatWqoVq1avy/ZW+EMqUzYMAA/Pvvv7hw4QI8PT0xdepU/Prrr3KFPsHBwcjIyIChoSH7zX9H0vMge3t7XLt2DWFhYdi3bx+GDRsGfX19ud/3lStX8OrVKwwYMADq6ursuuA7+PQhoqKiItavX4/ly5dDU1MTffv2BfC/osQbN27g2bNn6Ny5M5SUlFgG30FRGbi7u8PHxweqqqqwt7eHjo6OXFHDnDlzkJmZiVq1arFrg+9Iuj+aP38+dHR0EBsbCzU1NYwZMwb169dHzZo1+XEfPHgAoKB1Deb7YPsj4SQnJyM6OhovXrzAli1bIBKJ0LJly0JFDW3btsWYMWOwYcMGHDt2DCKRCLNmzYKKigqMjIxgZGQEgN23+5Lk5GSoqamhUqVKcvcqvL298fLlSwwfPhz169fnx9fV1cWYMWMQFhaG69evo1u3bqz1EYYpqR/dpwXDMCUn2099UlISxcbGUlxcHP+ZbL97xSHbJ1NgYCCZmprSihUrvn1GKxhp32Cf9ssmFotp0qRJZG1tTREREUQkn9Hly5f5vog9PT3p/fv3ZTfTFZjsOo6Li6PIyEgKDw+XG+fDhw+0bt06srKyorZt21JgYKBcH2+f62OP9b1XPJ/253b58mXiOI5u377Nf/b+/XtydnYmjuOoe/fuFBwczO+jMjIy6N69e3Tu3Dl69OgRJSQkfHbazNd5eHgQx3Hk5OQk9/nr169p0KBBxHEcDRgwgJ4/fy43XLotRUZGUkBAQJnNb0V25coVGj58OJmZmdHEiRPpzp07/DDZ3/bbt29p/PjxxHEcNW7cmPbt28cPk+6H2LZQcr6+vsRxHI0YMYJu3rxJycnJFBoaSm5ubsRxHLVt25ZOnjxJRAV9565du5ZsbGzIzMyMLC0taeDAgXy/6UTsmFBaUVFRNGvWLDI2NiaO42jz5s38MNlz1UuXLtHAgQPJxMSE5syZQ48ePRJidn9qubm5dPr0aerYsSNxHEcLFy7k+xeWSCSUl5dH/fr1IzMzMzp+/HihawVp37j+/v7EcRwdOnSozJehInrz5g3Z29sTx3FkampKzZo1o7Vr11J0dDR9/PiRiIj27t1LHMeRj48PEbH9zY8mPaZGRETQwIEDieM4GjVqFD18+JAfJzQ0lD9vunz5skBzWrHl5+fTiRMnqHPnzmRlZUWurq705MkTfnhISAg5ODiQubk5Xbp0ScA5rZg+7cO+cePGxHEceXh4UEhICOXm5tL58+f5/Zf0nIn5fmQz2L9/PzVt2pTPIDk5mR8m23d9Ud9lvp+srCx+fcuu47CwMOrduzc1bdqUwsLChJq9Covtj4Tx5MkTmjFjBnEcR3Z2dnTlyhX++kD2XDQqKoqsra3JzMyMOI4jV1dXtg8qpuTkZFq2bBmdOHGCMjIy+M/PnTtHlpaWxHEcWVtb06pVq+jixYv88IyMDHJwcCAzMzO6f/++ELPOMD81EVERHbswDCMY2cpHX19fHD16FDExMfjll1/QunVrLFmyBEDx396Rnd4///yDDRs2IDMzE3v37uX7F2WAq1ev8pWpnzb5lJqail69eqFKlSo4evQo/xYu8L+3s86dOwcXFxd8/PgR06ZNw4ABA/g+WJmSk/1979y5EwcPHkRMTAy0tLRga2uLv/76ix83PT0d27Ztw549e6CtrY3Jkyfjjz/+wLNnz/Ds2TM0b94cmpqaQi3KT0s2gydPniArKwsPHjzAkSNHEBAQAJFIxL9dm5ycjBUrVuDkyZMwNDTE/PnzYWNjA1VV1SKnTawCuVSioqIwdOhQpKen832gS7158wZTp07FnTt30LBhQ6xZswZ169blh396zGBvgJaO7Hq7ceMGfHx8cOPGDXTs2BFjxoyBlZUVAPlj75IlS3DkyBHk5ORAIpHgxIkTclX6TMm8fPkSI0eOhFgsxubNm2FsbMwPW716Nfz8/NCmTRssWLAANWrU4N+UfvnyJcRiMRQUFKCpqck3D8/eOCk52XX2+PFjBAQE4O+//4aOjg4WLVrEn0fJvg19+fJl+Pr64vbt2+jUqROWL1/OZ8AUT25uLv79918sW7YMr1+/xsCBAzFv3jz+WPv3339j+fLlaNSoEebOnQuO4yASieRy8PT0hJeXF3x9fdGqVSshF+enJ3suc+nSJYSHh8PPzw9isRg1atSAtbU1Jk2ahNzcXCxcuBDx8fHYu3cv34oS82Pl5+cjNDQUmzdvRmhoKJSUlPjf/K1bt5Cdnc33X8/8GNnZ2Thx4gR27dqFmJgYVKpUCS1atEBeXh5u3ryJ3NxczJ07F8OHDxd6Visk2WN1YGAg1q9fj9TUVCgpKaFWrVp4/vw5KleujAkTJvDbAbtG+75kM/j777/h5uaG1NRUTJ48mW+pAWDr/Uf7dP1GRETg2rVraNGiBeLi4rBjxw48fvwYrq6usLe3F3BOKy62P/pxvrSeYmNjsWXLFpw8eRKWlpaYPHky31KD7PWBg4MDLCwscPjwYYwZMwajRo0qy0X4aWVmZqJv375QVFTEzJkzYWtri2PHjkFZWRn169fHwYMHcebMGSQlJQEo6JKxS5cuaN26Nc6fP48ZM2Zg6NChmDVrFpSVldnvnWGKiRU0MEw5Je2nXltbGzo6Onj79i0+fvyIrl27YuPGjQC+/kDq02IGNzc35ObmYu/evXzTUUyBNWvWYMeOHejUqRM2bdrEr1eJRIKMjAzY2dnh3bt3CAgIkHt4InvyuGLFCuzevRtKSkoYNWoUhg8fzooaSkH2d7t27Vps374d6urqqFevHuLj45Geno4uXbpg06ZN/HekRQ0BAQHQ1tZG48aNERERAaBgWzI3NxdkWX5Wshls3boVe/bsQUpKCoCC/c7evXvRqFEjAP97YPVpUcOCBQvQpEkT1ifudyLd3z958gRDhgxBamrqV4sa1q5dizp16gg30xXA1x52BwcHw9fXFzdv3ixU1CDNbOXKlUhISEDHjh2Rnp7Obtx/o9u3b8Pe3h6TJ0/G5MmT+c89PT3h6emJ1q1bY8GCBVBQUMCWLVswceJE1K5dWy5L6bGb3Sgrnk+3g7y8PCgrK/P/joqKwp49e/DPP/+A4zhMmzYNHTp0ACBf1HDhwgVs2LABAwYMwLBhw8p2IX5y0v2JWCxGSEgIXFxc8P79e/Tr1w/z58+Hmpoanj9/jtWrV+Py5cto06YNxo0bh0aNGvHZ3b59G7Nnz0Zubi58fHzkzmeZrytO8VNkZCSCgoJw5swZxMbGQkdHB7169cKTJ09w//59zJ49G3Z2dqyosIxIJBK8ffsWPj4+OHfuHD58+AB1dXUYGRlhwIABfBderLDt+5MeX3Nzc/Ho0SPs3bsXZ86cQW5uLjQ0NMBxHAYNGsQy+MFk1+v+/fvh7e2Nt2/fwtraGhMnToSmpiYsLCwKjct8P0UVNWRmZmLs2LFwcHBg94vKWHJyMubMmYOrV6/yn6mqqmLWrFlwcHAAwB6k/yhsf/T9yZ5PZmRkICUlBR8/foS+vj40NTWhpKSEx48f893/WVpaYtKkSWjRogVf9B8UFITx48djz549sLGx4X/7bDv4uuTkZCxduhQXLlyAoaEhDAwMcOXKFUyfPh1jxoyBgoICoqOjce/ePWzduhWvX78GEaFZs2aYNm0aFi5ciKysLOzbtw+6urpsnTNMMbGCBoYpJ2RP2O7cuQNHR0fY2Nhg5syZMDAwwKNHjzBt2jSkpqbKPcz93A2xzxUzBAQEgOO4sluwn8iRI0fQrl076Ojo4NmzZ6hXrx4/bNmyZQgICMDkyZMxYsQIuTf+pTf2jxw5ghUrVkBbWxuvXr3C/v37+QdbTMn5+Phg/fr1aNmyJaZNmwYLCwtERERg1KhRyMzMRMeOHbF582Z+/PT0dOzYsQOBgYF4//49AGDOnDkYOXKkUIvw05M+INTS0oKuri5fWNWlSxc4Ozvj119/BVB0UUOdOnUwf/58tG3blp2Ul9CnF/DSf0v397GxsbC3t/9qUYOpqSnWr18vty9jik/2+BoZGYm4uDg8evSIv1ht3rw5AODmzZvw9vbGzZs30aFDB4waNQo2NjYACh4gSh/uLl26lJ82u0lTetKbLrIFDdJ9VatWrTB9+nSYm5tj7ty5OHz4MNatW4devXoJPNc/L9nt4NSpU7h//z5u374NOzs72NjY8G+bP378GH5+fjh8+DBMTU3h5ORUZFFDfHw83z80u2lTPLIZBAcH4+HDhzh9+jQePnwIdXV19O7dG/PmzYOamhoiIyOxevVqhIaGon79+mjRogXatm2L58+fY//+/Xjy5AkWLVqEwYMHC7xUPxfZDG7duoWnT58iIiICxsbGqFOnDjp27MiPK5FIkJ2djd27d+PGjRu4efMmNDU1kZGRARsbG/j7+7NiBgEkJCTg48ePqFSpEtTV1VkrPWXg0318TEwMsrOzUblyZWhoaKBq1aoAWAY/muz63bdvH9zd3ZGWloagoCAYGBgUGof5/mTX74EDB7By5UpkZWUhMDAQlpaWAs/df0teXh6ePn2KU6dOISkpCbVq1UKTJk346zq2LfxYbH/0/ciem+7btw9nz55FZGQksrOz0aBBA5iYmMDFxQXa2tp48uQJNm/ejJMnT8Lc3ByDBw9G7969ER4eDg8PD7x48QI+Pj58K84sg897+vQpqlWrBi0tLQDA+/fvsXXrVuzbtw9isRjm5uZYtGhRoZfq4uPj8fDhQ+zZswehoaFQV1eHiooKUlNT4ejoiNmzZwuxOAzzU2KvTTJMOUBE/MlCYmIiYmNjIRaLMWXKFBgbG0MikaBFixbYvXs3hg4dinPnzmHKlCl8SwKfFjWwYoaSkd5o79u3LwBg3rx5OHToELy9vdGuXTsAgK2tLS5duoT9+/fD0NAQHTp0gJqaGiQSCf+W4rt376CgoIBhw4ahWrVqrJihBD49YY6MjOS7RZk1axZMTEwgkUhQo0YN6OnpIScnB5cuXeK3AwDQ0tLCqFGjYGZmhoSEBNSqVYu/wcwemhSP7L7k7du3OHbsGFq0aAEXFxdwHIddu3bhn3/+weXLl6Gvr49hw4ahdu3afJN1urq6mD9/PvLz83H27FmkpKSw9V5CsseDsLAw2NjYQEFBARKJhN/f169fHwEBAbC3t8eZM2cwbdo0vqihevXq2LhxIyZNmoT79+/j0aNHrKChFKTrGwA2b94Mf39/fPjwQW6ckSNHYvz48fwNMJFIhMuXL+P9+/fo3r07NDQ0EBAQgPfv36N169Zy32U3CEpPQ0MDABAdHQ1AvphhxowZMDU1BQC+4OrT3Jjik90ONm3aJFdEGB0djR49esDe3h7m5uYwMjLiCwgPHz4MDw8PAECHDh3kmjVlxQwlQ0R8BuvWrYO/vz8AwNzcHHp6evwDEaDg/NXc3Bzz5s3D/v37cenSJezevRu7d+8GAOjq6sLV1ZUvZmAZFM+n24Gfnx+ysrLkxhk4cCCGDBkCIyMjKCgooFKlShg7dizGjRuHI0eO4PLlywgJCcHt27dx6NAh2NnZCbEo/0nSa4waNWrIvXUo/S87Hv840vUtzaB+/fosAwFIryMUFBQwePBg5OfnQ0FBgX94KB2H+XFkM7Czs0N2djaUlZVZMYMAlJWVwXFckfdF2UPcH4/tj74P2esDaau2mpqasLKyQnJyMt68eYNHjx7h/v37cHNzQ4MGDTB+/HgoKCjg7NmzmD9/PjZt2oTXr18DKLiGkO2SmmVQtIiICAwcOBAtW7aEl5cX1NTUULVqVbx9+5a/1k1LS8P79++RnZ3NPzNQUFBA7dq1Ubt2bXTt2hXnz59HcHAw/vnnHygpKSEsLAxpaWnQ1tZm12cMUwyshQaGKUfc3d1x4cIF1K5dGwkJCTh27Bjy8vKgpKTE30yTfTO3qJYaWDHDt1u4cCF/c1ha1JCTkwMvLy/4+PigevXqmDx5Mtq2bQt9fX0ABa1qLFiwANra2vDz84OamhoAdlH0JXfu3MHjx48xcOBAAPLr6sSJE5g5cybc3NzQo0cP/jvz5s3DpUuXsGLFCsybNw+pqano3Lkz3N3dP9u1Acvg86RvqgHyDzZu3LiB9+/fY9asWdi2bRvatGkDoKD/7lOnTmHbtm149eoV7OzsMHz4cP4BlXQ/9P79ezx48ABt27YVZsEqgE2bNmHLli2YPXs2RowYAZFIVKilhsePH8PBwQFpaWmFumF5/fo1wsPD0b17dwGX4ucn7f7JyMgIgwcPBhHhzZs38Pb2BhGhZ8+emD9/PnR1dXHjxg3s27cP586dk5sG6x+65L60387IyMCECRMQGRmJJk2a4N9//y1UzAAAGzZswNatW7Ft2za2LyoF2WOCdDuoX78+Ro8ejTdv3uDMmTN4/PgxevToAQcHB/6mvGxLDRYWFhg/fjw6deok5KJUCAcOHICrqyvat2+PCRMmoGHDhnj+/Dnu37+PDRs24NWrVxgwYADfUkNycjLS0tJw4cIFZGdnw9DQEHXq1IGZmRkAdm5UGl5eXvDw8ICVlRUcHR2hoaGBpKQkbNmyBS9fvkSXLl3g5OSEBg0aFPpuWloaYmJiMH78eLRt2xbr168XYAkYhvkvK2q/z44FZYtlUD7Idj0HgD08FADbFr6Pffv2YcmSJWjfvj0mTpwIS0tLpKWlITk5GfPmzUN4eDjq16+PXbt2oWrVqnj27BlOnz6NY8eO4e3bt6hfvz4GDx7Mv9zHHqZ/WXh4OIYMGYJmzZph69atUFdXB1DwDOHRo0eoWbMmLly4gHr16mHy5Mno0KEDVFVV+fX66cuoN27cwOnTpxEYGIiVK1eif//+Qi0aw/xciGGYciEzM5PWr19PFhYWZGxsTJ07d6b8/HwiIhKLxURE/L9jYmKoadOmxHEcTZs2jZ9GXl4e//eBAweoRYsWZGNjQ1FRUWW4JD8viUTC/7127VriOI44jqNLly4RUUFGK1euJGtra2rYsCH9+eef5ObmRsuXL6d27doRx3G0d+9eoWb/pxIfH0+WlpbUuHFjCgwM5D/Pzc0lIqJVq1YRx3Fyw3x9fYnjOPLw8CAioqtXr5KVlRVxHEfjxo2jmJgYSk9PL9sF+YmFhITQuHHjKDw8XO7zwMBA4jiOHB0dqX379vT8+XMiIsrJySGigowOHz5MPXr0IEtLS1q+fDnFxcXx35fdDxH9b//FFF9GRga5ubmRlZUVtWnThvz8/Pj906fHg2vXrvHbweTJk4ucHsugdC5cuECmpqY0dOjQQsfR4OBg6tWrF3EcRwsXLuQ/f/bsGf3999/k6OhIbm5uFBQUxA9jORSP9LdNRBQaGkonTpygK1eu8PsiIiI/Pz8yNzcnjuOoX79+dOfOHblphIaGUvv27alDhw4UGxtbVrNeIR09epQsLCxo9OjR9PDhQ/7zLVu2EMdxZG5uTs7OznT//n1+WHR0NM2fP584jiNbW1u5YwRTMhKJhHJzc2nixIlkZmZGkZGRhcaJiYmhLl26EMdx5OrqSllZWV+dJlMyd+7cocaNG1OfPn3o0aNHcsNcXFyI4zgaNmwYJSUl8Z9LJBJ+XUskEsrJyaHx48cTx3F09+7dMp1/pvAxWPZYw5QNloHwPr1OYxmUPZZB+cD2R8Jj20LpSSQSSk9PJwcHB2rUqJHcuan03DM9PZ0cHR2J4zhycHDgrw/y8/Pp48ePlJiYSCkpKfz32L2KL5Ou1ydPnvD3nY8fP86v18zMTEpPT6cFCxaQqakp9erVi86cOUPZ2dly3yeSX9cRERFkZmZGjo6OlJ2dza7TGKYYWOkbw5QTlSpVwtChQ+Hk5ITKlSsjLi4OGzZsgFgs/mxz47q6ujh9+jRGjx4NAPwb6qxlhtKRVkwCwMyZM+Ho6AgAmDBhAi5fvoxKlSph2rRpcHFxQbNmzXD//n14e3tj9+7dUFVVxbJly+Sa8WU+T0lJCUOGDIFYLMbmzZuxf/9+AOC772jSpAkqV66MzMxMAMDFixf5t2ylfaE3aNAA1apVg5KSEoKCgjBo0CA8e/ZMmAX6yWRkZGDLli0ICgpCSEgIJBIJP6xevXpo06YNgoODkZiYiPDwcACAiooKxGIxlJWV0bNnT4wZMwa1atVCYGAg9uzZg/j4eAAo1FIGq7IvOQ0NDTg4OGDChAnIyMiAr68vdu3axTfLKz0eEBGMjY1Ru3Zt6Orq4vz580W2BMAyKJ179+5BLBZj3Lhx4DgORMRvKy1btsSCBQtQpUoV/P333/j7778BFHRzMGDAAPj6+mL69Ol8t0XsjZPiIZnmMz09PTFixAjMmDEDY8eOxdy5c3HlyhUAwIgRI/DHH38AAJ49e4b79+/j7t27SE5OxqlTp7B8+XIkJiZi8uTJMDQ0FGx5KoKgoCCoqanBycmJb4pULBYjLCwMOjo6MDMzw6lTp+Dn54d79+4BAIyMjODg4ICuXbvC3t6eb8WHKTmRSISMjAxERkZCX1+fb4VE9jyzfv36WL9+PfT19XHgwAGsWLECOTk5AAq6VStqmkzJPH36FOnp6Rg9ejSMjY35zz09PXH48GG0bdsWK1euREZGBt/Fh0gkkmtyX0VFBQ0bNgRQ0E0d83kk0x3Bp5+Vhuwx+PTp03j//r3cG3JMYSwD4f2IDKTXaSyD4mEZlA9sfyQ8ti2ULyKRCO/fv0dYWBgsLCxgbGzMn/NL72tramrC3d0dv/32G27fvo3g4GAABbmpq6vDwMAAlStX5j9j9yqK57fffoOmpia8vb0xc+ZMzJs3DxkZGahUqRI0NTUxfvx49O/fH0+fPoWnpyeCgoKQlZUld/0lbXVVIpHA0tISNjY2CA8PR3p6OrtOY5hiKLp9boZhfiiSaeJM9mBVrVo1/mGtt7c3Dhw4gKpVq2L48OFFFjXs2rULvXv3hrm5OT+NmzdvYs2aNSAiVsxQCtL1q6ioiNmzZwMAduzYgQkTJmDLli3o0KED/vjjD9jZ2eH27dvIysqCtrY2dHV1+Rv27MHV1xkYGGD48OFQVVXFjh07sGXLFgDAoEGDAAAmJiaYOXMmWrRoAQA4evQocnJyMHr0aL5f9KpVqwIoeHBiYWEBU1NTWFhYlP3C/IQ0NDTg6OiIRo0aYdCgQVBQUMDr16+hr6+Pxo0bQ1FRERoaGjhz5gw2b96MGjVqoHnz5vz2IS1qAIDt27fD398fmZmZcHFxgaampsBL93P53P6iWrVq+P3330FE8Pb2ho+PDwDwx4O8vDwoKyujatWqqFy5MurWrYukpCQ0a9asrBehwpFIJJBIJLh69SpEIhH09fUhkUggEomgoKDAH7ubNWuGBQsWYObMmbh48SJ69+4NVVVVvtjk04tW5uuk62zPnj3w9PRE3bp1YW1tjefPnyM8PBzLly9Hfn4+OnXqhMWLF0NdXR2HDh3CihUroKCgAC0tLaSmpkJVVRXz5s3jm238NA+meFJSUnDt2jVwHCfXz/PGjRtx48YNuLu7o1KlSli0aBHOnTsHiUQCBwcH2NjYwNjYGIsXL4aOjg4AlsG30NbWRtWqVfHmzRt8+PABOjo6/DWBFMdx6NatG3bt2oVz585BLBZj4cKFfBdoTOlI+8ONjIwEAL6bLqCgmMHT0xOtWrXC5MmT8csvv6Br16548eIFGjVqxHfvAYDP6u7duwAKuvxiiibbHG9+fj7S0tKgqamJvLy8Up1jyp5nHTp0CPPmzYORkREOHz4MBQUFtl8qAstAeCwD4bEMygeWg/BYBuWTsrIyVFRU+GxkXyxSVFREfn4+tLW1YWdnh9WrV+PJkyfo1KmT3HjSdc3W+dd9uo6srKxQrVo1nDp1CkpKSnwX1L/88gsmTpwIoOD37eHhAQD8NUJCQgKaNGkil0N2djYUFRWRlZVVdgvEMD8xVtDAMGVM9mQwLS0NaWlpyMzMhI6ODvT19VGjRg306dMHRIStW7cWeoglW9TQoEED3Lp1i6+qBIDGjRujQ4cOGDlyJCtmKKVPixpEIhG2b9+OCRMmYOvWrWjfvj0AwMbGptBJDatsLR4igoGBAQYPHgwNDQ14enpix44dEIvFsLe3R40aNfD7779DRUUFz58/x6VLl9CxY0c0bdqUfyhy/vx5xMXFYdmyZbCzs+OnzQpKvk4kEqF169Zo0aIFFBUVsWbNGoSHh2PevHmwtLSEtbU1gIL91fnz5+Hp6QllZWXY2NjIFTX06tULRAR3d3cYGxuzYoYSkj0e3LhxAwkJCcjJyYGJiQmsra2hr6+Pvn37QiQS8ccDIsKIESP41kyCg4MRHh4OLy8vft8EsIeH30JBQQEKCgr49ddfERUVhdTUVL6QAYBcQWLTpk2hq6uLuLg4uQeMbB9UOtL999WrV2FsbIz169fD0NAQT58+xc6dOxEYGIi//voLRARbW1vMmTMHzZo1w927d3Hz5k0AQLNmzdCkSRO0bt1abppMySkrK0NbWxtxcXFITExEjRo1cPDgQfj6+uL3339H8+bNoa2tjU6dOsHf3x9BQUGIiYnB77//jjFjxrBihhKSXU/Sv/Py8iCRSNCgQQM8fPgQ/v7+mDp1KhQVFfnfNhFBWVkZJiYmUFBQQG5uLg4dOoQ+ffqgefPmAi/Vz+XTvm2lNxulLb2kp6cDADw8PODl5YVWrVphxowZfMsZhoaGePHiBd/im1R+fj68vLwQFBQEPT09NGnSpCwW56cju/4PHDiAixcv4s6dO9DS0kLlypUxevRoNGvWDLq6usWanuz+X9qCYeXKlbFq1Sr2JuhnsAyExzIQHsugfGA5CI9lILzPXUdJJBIoKysjODgY//77L9q2bSs3XLo+pS/eJScnf3F6zOcVtc6aNWsGd3d3zJw5E8eOHQMRwdXVFdra2qhRowYmTpwIkUjEFzVcv34dYWFhUFJSwtq1a2FkZIT8/HxMmjQJERER+O2336CtrS3QEjLMz4UVNDBMGZI9Gdy7dy9Onz6N6OhoZGdno0aNGrC2tsbs2bOhr6+PP//887Nv5so+NJEe8CQSCYgISkpKWL16tTALWIHIFjXMmjULQMGb6OPHj+e7PigKOzH8OtntICUlBerq6qhTpw5iYmKwb98+AIC9vT1UVFRAREhPT0d+fj7/AFckEuHOnTvYsWMHqlevjnr16slNnz24Klp8fDzOnTuH4cOH8zfoFRUVkZSUhODgYERHR2Pz5s2YNGkSLCwsYG1tjVGjRkEsFuPSpUvYtGkTpkyZIlfUoKSkhF69esHS0pK/2c8ukIpHtml9Dw8P+Pj4IDc3FwBQp04d9O/fH+PHj4e+vj5+//13AAUt93h7eyM5ORkjR47E3bt3sX37dmhoaMgVk7AMiq+oFpOk+6g6depAIpHA29sbS5cuhYGBQaHvV69eHSoqKlBWVmb7nlKSPSYoKCggIyMDsbGxGDlyJL9fqVevHv+mQ2BgIFatWgUAsLW1Rfv27dG+fXvk5ORAJBJBRUWFnzYrZiiez60nTU1N9OjRA8+fP4eKigqio6Ph6+uL3377Dfb29vw5aIMGDSASifDLL78gNjYWeXl5ctNh+6Ov+zSDzMxMaGpq8uc+dnZ2OHr0KLZs2YLq1atj8ODBUFBQQH5+Pr/9vH37FgYGBli5ciXevHnDihlKQbou3dzcYGFhgU6dOkFBQQG1atUCAKxatQp3797F/v370apVKzg7O/PFDMD/mlCWbckBKCiMsLGxwZQpU9CjR48ijyf/dbLnRWvXrsX27duhrq4OjuOQk5ODBw8ewNnZGf369cPgwYO/2ipbUQ9NWHeMX8YyEB7LQHgsg/KB5SA8loHwZK+TMzMzkZKSwndtUKtWLYwYMQKenp44evQofvnlF/7aWdqaJwC8fv0aAPhuz9h1Wcl8LgMNDQ00btwYbm5ucHZ2xvHjxwGgUFGDsrIyDh8+jNjYWACAi4sLjIyMABRcH9SsWRPNmjXDokWL5F5WZRjmC4hhmDIhkUj4v9esWUMcx1GjRo3I0dGRBg0aRE2bNiWO48jOzo7u3LlDRERv374lb29vsra2ppYtW9LOnTvlpsP8ePn5+fzf0tw4jqPz588LOFc/L9nf77p166hp06ZkYmJCnTt3Jo7jyNjYmDp06ED79u3jx4uKiiJra2viOI7++usvWrVqFdna2hLHcbR//34hFuOndOrUKerYsSP5+Pjwn92/f5+IiMLDw8nR0ZE4jqMxY8bQvXv3+HHu3LlD48ePJ47jaNiwYRQWFsYPy8vLk/t/sP1TyW3ZsoU4jqOOHTvS8uXLaeTIkWRlZUVmZma0YcMGfrykpCTy8fHhjxWNGzfm90c7d+4UcAl+XrL793fv3lFcXBwlJSXxv+OUlBQaMGAANWzYkNzc3Ojt27dEJP+7DwoKIo7jaNGiRSSRSEgsFpftQvzkZDM4fvw4/fXXX+Tm5kbNmzenLVu2FBonKSmJXF1dieM46tSpE126dIkfxtZ96ciu34iICDpx4gTt27ePTpw4QUQF6zUlJYWIiA4ePEgcx9GRI0fkprF27VqysrKiuLg4evXqVZnNe0Uhm8Hhw4dpxowZZGtrS0uXLqWHDx/y+5w9e/YQx3FkbW1N27dvl5tGeHg49enTh/r160fp6en852y7+DzZ9S5rx44dxHEcf44k5eLiQhzHkYmJCQ0ZMoRevnwpNzwkJIQaNmxIAwcOpDdv3hR5TvS5/yfzPwEBAcRxHI0dO5YePXrEf37u3Dlq3LgxWVpakre3N2VlZX12GrK/+wMHDlCLFi3IxsaGoqKifui8VxQsA+GxDITHMigfWA7CYxkIQ3ad7dmzhwYOHEgcx1HTpk1pyJAhlJCQQPfv36chQ4aQsbExLViwQO4+HlHB9UGvXr2oTZs2ctkxxfOlDAYPHkxxcXFEVLCe27VrRxzH0cyZMyk1NZX/3uvXr+nQoUO0adMmOnfuHP95bm4u/3dGRkYZLA3DVByshQaGKSPSKsj9+/dj+/bt6NChAyZOnAgLCwvk5ubi1atXWLhwIUJDQ7Fs2TK4ubmhbt266NOnD4CC1gG8vLyQnZ2NcePGCbkoP5VvfTvz05YaxGIxdu7ciZcvX37HufzvkG4Hu3fvho+PD9q2bYuRI0fC3Nwc9+/fx5UrVxAYGAgvLy8AwKBBg8BxHObMmYNFixZh586dAAANDQ0sWrQIAwcOBMDeSC8OIsKrV6+wY8cOVKpUCc+ePcOVK1fw119/wcbGBhMmTAAR4d9//wUAODk5wcLCAlZWVvw+5/Lly/Dy8sLEiRPRuHFjuX7fAFbtXRyyFd5v3rzBqVOn0KhRIyxatAgcx+HNmze4dOkS1qxZA29vbwDA1KlToa+vj/79+6NBgwbYuHEjcnJy0LBhQ/Tr1w89e/YEwN5GLwnZHPbv349jx44hNjYWWVlZWLFiBbp16wYtLS0MGTIEGzduREBAADIyMjBixAi+2cbQ0FD4+vpCRUUFHTp0gEgkYttACcm+Db1t2za5YU+ePOH7r5f+V19fH5MmTQLwv5YaiAgdO3Zkv/1SkG3xa+vWrdixYwfS0tL44fHx8Rg9ejSqVKmCvLw8XLp0CQDk3qK6desWDh06BBMTE2hoaKBKlSr8tFkmXyebgbu7O7/fB4CAgADExsZi7NixaN68OQYMGICPHz9i/fr1WLNmDUJCQlC7dm0oKyvj9OnTSExMxNKlS+Va7GEZFO3q1as4fPgwXF1d+a5RpOLj4wGgUKs906dPx5s3bxAcHIykpCRkZWXxb8AFBQVh69atyM7OhoODA6pVq1bk/5c1p/xlGRkZOHPmDLS0tDBp0iQYGxvzw2JiYpCeno4WLVqge/fuyM3NRX5+fqHuztgboN+GZSA8loHwWAblA8tBeCwDYZBMV8br16+Hj48P9PT00L59eyQkJODdu3dQVFSEubk5Ro4ciaysLBw8eBD37t1Djx49UL9+fSQmJiIgIADPnj3DkiVL5LJjvu5rGSQnJ/Pn9dbW1nB3d8f06dMLtdRQvXp19OvXT27a0u5CpPexNTQ0ynbhGOZnJ2AxBcNUWDk5OYU+k0gklJ6eTkOHDqWGDRvSw4cP+WHSt3WSkpJowoQJxHEcjRo1ih/+5s0b2rx5M3EcR3///fePX4AKIDExkf/7e7wNJTsN2TfUmZKRSCSUnJxMAwcOpMaNGxeqyE5LS6NDhw6RlZUVtW7dmvbs2cMP+/fff2nHjh10+PBhunv3Lv85e/uweBITE8nPz4+aNm1KVlZWxHEcTZgwgZKSkoioIJvQ0FAaOXLkZ1tqmDRpEnEcR/3796fXr18LtSgVwpUrVyg0NJTMzMwKve2cmZlJgYGB1KhRIzIxMZFrqUE6PD09nb2FW0qfthTDcRxZWVmRnZ0d9ezZk86ePcsfxzMyMmj//v3UpUsXvhrfycmJxo8fTw0bNmQtZJSSbAb//PMPmZiY0ODBg8nb25vWrl3Ltz7i5ubGjyfbMkZSUhItXryYzyQ+Pr5M57+i8fT05FsJ27dvHwUGBtKMGTPo+fPncuMtWbKEOI6j1atXU1JSEl27do0GDx5MVlZWdPLkSYHmvmLYsGEDn8H58+fp+vXrfMtJw4YNo2vXrvH7+fPnz1OPHj34Y7mZmRnZ2trKtVrFWkwqmkQioYyMDOrYsSNxHEczZszgWyCR7mOk+5Zbt24VWo8xMTE0btw44jiOTE1N+VYximoxiWVQcnFxcWRlZUVOTk5yn3t4eBDHceTo6EixsbH05MkTGjRoEF24cEFuPPYG6LdjGQiPZSA8lkH5wHIQHstAWEePHuXvzUnXWV5eXqF7cUFBQTRjxgz+fFTaolizZs3k7qmyc9OS+1oGsuv0zp07fEsNc+bM4VtqYOudYb4v1kIDw3xnt27dwunTpzFq1Cj+LU6g4C2flJQUREREwMLCAiYmJnylqqKiIiQSCfT19bFo0SLExMTg2rVrOHLkCPr27Ytq1arhjz/+QOvWrb/aLxlTUEW8efNm1KtXD8OGDSv0NhSV4m1+2ZYabGxsAIB/Y5QpPpFIhLS0NERFRcHS0hIcx0EikQAoeItQS0sLvXr1Qk5ODlauXIldu3ZBLBZj2LBhaNOmDdq0aSM3PfYGaPEZGBhg6NChOH/+PG7fvg01NTXUrVsX+vr6AAqysbGx4fupL6qlhlGjRiEtLQ0dO3ZE9erVBVuWn93Bgwcxf/58mJmZQVdXl38zQfqmZ6VKldCrVy8ABf11y7bUAABqampQUFAAEQGQrx5nvk66/w8ICICPjw86dOiAyZMnw8zMDC9fvkTNmjWhoKCAzMxMaGhoYODAgTA2Nsb27dtx+fJlnDt3DhoaGjA2NsaQIUP4lpTY/qh4Pl1PDx48gL6+PpYsWYIGDRoAACwsLDB16lR4e3tDSUkJTk5OhVpqGDduHDIyMmBsbMz3b8+UXFhYGPz9/WFqaorly5fzfXr27duX73sVAHJzc9GxY0cEBwdjx44dCAwMREZGBgBg7ty56NGjBwDWYlJpXLhwATt37kSrVq0we/Zs/pjw9u1bhIWF4datW8jNzQUANGvWDLa2tjA3N0dycjKio6Px66+/QkdHB7/++isAti/6EulbUOvXr8ecOXNw4sQJiMViLF68mO+3VktLC0DBsVb6W5b+ruvXrw8PDw9s3rwZ169fR1xcHNTV1dGtWzf07NkTnTt3BsAyKK3MzEzk5OQgNTWVPyfy9PSEp6cnWrVqBWdnZxgaGsLPzw937tzB+fPn0alTJwDsDdDvhWUgPJaB8FgG5QPLQXgsA2Fdv34dqqqqmDx5MjiO46+FZe/FJSUloVatWli0aBF69uyJ27dvIyUlBY0bN0a9evVgZWUFgJ2bltbXMhCJRIiPj0dmZiasrKywbt06zJkzB0eOHEF6ejrc3d2hoqIi8FIwTMXCnsQxzHf04cMHuLq6Ii4uDqampnIFDQCgpKQEFRUV/iRC+kBKJBJBQUEBYrEY+vr6GDt2LFxdXZGUlMR/t3r16vwBk52IfN2dO3dw4cIFqKmpYfDgwQgICEDlypXRq1evUt9ol/1ebm4uVFRUWBaloKqqCnV1deTl5QGA3INZAFBWVkb79u1x+vRp3Lp1C//88w9EIhGGDh1aaFps3ZfM9evXcf/+fTRo0ACvXr3C0aNHoaurixEjRkBZWfmzRQ1TpkyBubk5rK2tsX79er4pZfbgqnQMDQ3Rtm1bhIWF4ePHjwgKCoKxsTGUlZX5fYq6urpcUYOvry8kEgmmT5/O/+6l655lUHIJCQk4cOAAqlevDicnJ5iamgIAatWqhZCQENy+fRvHjh1D/fr10bFjR/Tr1w+bNm3Co0ePkJ2dDR0dHWhqakJPTw8AOy6XhHQ9LV++HB8/fsTLly/Rs2dPNGjQAGKxGCKRCF27dsWmTZswZcoUvguiT4saDAwMsGTJElSqVAkAy6C0nj9/jvT0dDg4OMDIyAgSiQQikUiuYPPp06e4ceMGjI2NMWHCBBw8eBCPHz9G27Zt0a9fP3Tv3h0Ay6C0bt26hby8PIwfP17uBu+5c+dQqVIltGjRgu/SID8/Hy1btoSBgQEMDAz4fZcUK3D7OiLibzhOmzYNp0+fBgAsXLgQOjo6/E1H2es06XFW2kTs1KlTMXz4cHz8+BHq6upQV1eHmpoaPw7L4Muk6/XT88hffvkF9evXx6tXryCRSLBt2zb+ocmMGTP437u0+C07O5v/rnSdBwYGwsPDgz00+QqWgfBYBsJjGZQPLAfhsQzKnw8fPiAoKAgGBgYwNjYu8oW65ORkbNu2DUFBQQgICECHDh3QoUOHQtNi1welU9wMdu3ahQsXLmDv3r1o3Lgx1qxZg9GjR8PCwoIVMzDMD8AKGhjmO6pSpQqmTJmC8PBwdO3aFQCQnp7Ov+mTn58PRUVFhISE4OrVq2jTpo3cyaL0BEPal+urV68AyPf1LTseUzQiQps2bbBv3z54e3vj1KlTCA0Nxbhx4+TyKAnZm5OHDx/G3bt3MX36dL6vaKZ4iAhKSkrQ09PDnTt3cPr0aXTv3r3QxZOBgQEsLCxw69YtPH78GGvXrkWzZs34N0eZ0mnTpg1WrlyJX375BQ8ePMDGjRuxY8cOiEQijBw5EoqKioWKGq5evYr8/HxMmTIFVlZWrJjhO7CyssLkyZOxdetW/Pvvvzh//jwaNmyIFi1aQEFBoVBRg4KCAhYvXgxvb2906NCBr7JnSi83NxevXr1Chw4d+BsxSUlJOHToELZt24acnBwQEZ49e4agoCCoqKigZ8+eMDY2lntjV/pfdlwumaSkJBw8eBBZWVkQiUSoXLkycnJyoKqqyrfa06VLF3h4eMDJyemzRQ3SYgaWQelFRERAIpFAW1sbQMH5juyNmry8PERGRmLZsmUYOXIk5syZg759+yIlJQWVKlWCqqoq/z2WQcnl5OTg7t27qFatGho3bsx/vmnTJly4cAEeHh6oV68eXr9+jdDQUIhEIuTl5aFt27ZQUVEpdI3AjstfJz3ntLCwwIYNG/iiBrFYjNWrV/M3HhcuXAgDAwO0bt0a1atXR+PGjaGoqMhfR1SpUoW/DpDutwB2nfY1sr/Z7OxsZGVlQSKRQE9PDxoaGrCyskJgYCC6deuGxMREtG/fHhMnTpQr3omLi+PPV2U9efIES5cuhUgkwsGDB9l1w2ewDITHMhAey6B8YDkIj2VQPqmpqaFKlSrIyspCRkYGdHV1C42jq6uL3NxcJCQkIDg4GH/++WeR9+nY9UHpFDeD7OxsJCUl4fr16/jzzz9hY2ODc+fOsXunDPODsIIGhvlOpAeoXr16oVu3blBSUsKqVavw9u1bzJw5EzVq1EDt2rUxdOhQeHh4ICAgAHp6ejAxMQEg333B27dvARQ0KwugUJcJzJdpaWlh9OjRqFatGtavX483b96gQYMG6NOnD7S0tEp80/3TptLWrVsHJSUljB8/nhU0lJBIJIKenh6GDRsGV1dXbN26Fbq6umjWrBlEIhFf9CMSiZCdnY0GDRpg4MCBUFBQYBc/30h6oSp967927drIzs6Gt7c3tm/fDgCFihomT56MnJwcXL9+HUOGDJGbHjshLx3pscLS0hLjx4+HRCJBUFAQfHx8oKysjMaNGxcqaujRoweys7MhEolYMcN3It2nh4eH48iRI3j+/Dlu3ryJu3fvAgAmTZoEIyMjPH/+HO7u7rh//z569uwp97tnLWSUnoGBAfz8/DB79mzExcUhISEBz549g7Gxsdzvv3PnznJFDWKxGNOmTSv0ZgTLoPSk3XXcv38ftra2UFJSkrvpoqysDCMjI6ipqSEsLIxvoUpHR4cV9XwnysrKSExMxM2bN9GiRQscOXIEvr6+6NmzJywtLfnW26ZPn46QkBA8f/4clpaWcHd3l+sWhCm+oooapN0J1axZEwDw4sULPHz4EJcuXQIA1KlTB+rq6mjTpg309PTQoEEDtGzZkm9pj/k6iUTCX9cGBgbi0qVLiImJgUQiQe/evTFixAg4Ozvj1q1bePHiBapVq4bx48fD0tKSn0ZYWBh2796NKlWq8NfRUoaGhli0aBHMzc3ZdcNnsAyExzIQHsugfGA5CI9lUH6pqamhXr16uHz5Mvbu3QtHR0e+mB/43zOEzp07459//sH79+8BsOvi76m0GQBgLXkyzA/EtiiG+U5kTxqUlJTw5MkTHDhwACdPnoSPjw/f2kKvXr3QqlUrXLlyBb6+vrhz5w7/HaDg4crevXuhp6cHQ0PDsl+QCkJXVxevXr1Cfn4+RCIRkpOT+XUtfVhSHJ8WM7i7uyM/Px/bt29HjRo1ftj8V1TShx89e/ZE//79ER0dDXd3d1y5cgVAwXYgEolw584dXLlyBXXr1sUff/zBP0wvbm5MYZ8WRunp6aF///4YP348/5v28/ODWCwGUNB0WqNGjTBr1ixs3LgRtra2Qsx2hSN7rLC0tMSkSZPQrl07XL9+HZs3b0ZYWBgA+f2Uuro6Bg0axLaD74SIUKdOHQwcOBAvX76Ei4sLtm7dimfPnqFr167w8/ODk5MTunbtii5dukBFRQX379/ntw3m28k2+S5tMcbLywvJyckA5H//nTt3hqenJwBg69atiIiIEGy+K6LmzZsDAA4ePMgfi6UPe6UZGBsbo2bNmkhLS5PbDlhRz7dTVVXFuHHjMGDAAL47KD8/P1SvXh1Dhw6Fvr4+AEBDQwMA0KhRI+Tm5sLExIQVM3yjT4safvnlFxw+fBj79u2DSCSCm5sbPD09MXPmTFhaWkIkEiE6Ohq+vr5YtWoVkpOT2W+/BGQLn9zd3bFw4ULcunULVapUQWpqKkJDQ5GdnY0qVapg48aN+OWXX/D27VusWrUKp06dwo0bN7B3717MmzcPz549w5QpU+RaNZHur+zs7Ao9UGEKsAyExzIQHsugfGA5CI9lUH5J152joyP09PRw4sQJXL16Fbm5uQAKWtCTPkOIj48HAL4gl/k+vjUD6TUCK2ZgmO9PRLIdlzMM811dvHgRa9aswYsXLzBw4EBMnDgR+vr6uHbtGjZv3ozw8HDUqlUL9vb2qFGjBt69e4eAgAA8e/YMixcvxqBBg4RehJ8SEYGIsHjxYnz48AHVqlXDoUOHoKWlhbFjx8LBwQHA1yslPy1mcHNzY/2+fUePHz+Gt7c3Tp48CVVVVYwYMQK1atVCVlYW/v77bzx58gSrV6/G77//LvSsVmjJyck4fPgwtm7dCiUlJYwePRomJibYu3cvrKysMHr0aH5cVl38Y0RGRsLDwwNXrlxBy5YtMXHiRP5mAFvnP05iYiKCgoJw5coVaGhoYMiQIahTpw6qVavGv6F+9uxZODs7Y+LEiZg0aZLQs1yhSNfxvXv3MH36dLx69Qrdu3fH4sWLUblyZQDyv/9Tp04hMTERo0aNEnK2KxRpBm5ubvD19UWzZs0wduxYtGjRAsD/1v/Vq1cxYcIE9OzZE6tXrxZ4risesViMrKwsaGpq4sKFC5g8eTJ/HSDNaOfOnVi1ahX27NkDExMTvsCBNWH67YraFwHA5s2b0bFjRwAFXYPk5eXh1q1bSExMhIKCQqGWq5jiOXjwIObPn4/27dtj0qRJsLCwQFRUFKpVq4aqVavyecTHx2PWrFl8y0lSOjo6cHJy4tc/2wZKjmUgPJaB8FgG5QPLQXgsg/IrNTUV3t7e8Pf3R7169TBy5Ej06NEDampqAApeiFy4cCFev34NX19fNGzYUOA5rnhYBgxT/rCCBob5Dr50wnbx4kWsWrUK8fHxGDBgAKZMmQI9PT2EhYXh4MGDOHz4MD+ugoICKleuzE4GS6Goh375+flIT0+HsrIydu7cCV9fX2hra2PcuHGwt7cHgEJ9Dxc1PVbM8OO8evUKhw4d4vtHB8D3p+7k5MTnxLaDHys5ORlHjhyBt7c3UlNT+T7qFy1ahMGDBws9exWW7O9atqihTZs2GDNmDJo0acJ+92UgNzcXCgoKUFJSQl5eHv/Wc1hYGFauXInnz5/D09MTLVu2FHhOK57PFTUsWrRIrm/6T4/vnzt2M6UTFRWFLVu24Ny5c2jYsCEGDhyIfv36AQBCQkKwceNG3L59Gxs2bEC3bt0EntuKSbotzJ07F4cPH4abmxt69OgBAAgNDcW8efOgpqaGbdu28S2EsYK370e6/iMiIuDs7IxXr16hV69emD9/PnR0dD67rlkGRcvOzuZv9EoREbKzs+Hs7Izg4GDs2bNHrslqWXFxcahUqRK0tbVx5coVPHz4ECkpKbCwsADHcTA3NwfA1v+XsAyExzIQHsugfGA5CI9l8PNKTEzE1q1bcezYMSgqKsLa2hodOnTAu3fvcOTIESQkJGDBggX8i3vM98cyYJjyhRU0MMw3kr2pHh8fj8zMTOTl5cHIyAiqqqoAgEuXLuGvv/7iixqcnJxQrVo1AMCFCxfw+PFjxMfHo1mzZqhXrx5f0cdOBotHNoNnz57xb1FZWFjw4yQmJuLgwYNyRQ1Dhgwp9LBQ2qwUK2YoWxEREXj16hUePnwIc3Nz/PLLL3x+bDsoG8nJyQgKCoKvry8UFRUxZMgQVsxQxiIjI+Hl5YXLly+jYcOG2LRpE9/cOPPjhYWF4dy5c2jQoAHy8/Oxc+dOvHjxgl2c/mDFaamB+fHu3buHPXv24NixYwAAc3NziEQiREVFIS8vDy4uLhgxYoSwM/kfsG/fPixZsgS9e/dGnz59kJOTgy1btiA6OhorV65Enz59hJ7FCovti76P0NBQHDlyBPb29jA1NZUb9vr1a/Tv3x81a9bEgQMHABQuWk5JSYG7uztiY2Ph5eUFHR2dIv8/rNj581gGwmMZCI9lUD6wHITHMvj5vX79GmfPnsU///yDx48f85/XrFkT48ePx4ABAwCwe6c/EsuAYcoPJaFngGF+ZrIP0nfv3o2DBw/ixYsXyMrKwqxZszBo0CBoaGjwzZX+9ddfCAwMBABMmDABNWrUgK2tbZF908v2Z8Z8nkQi4TPw9fVFYGAg3r9/j6ysLIwePRp9+vRB/fr1UaNGDfz555/8eN7e3gAAe3t7xMbG4vnz57C1tZVb56yY4cu+x4ma9KKnYcOGaNiwIf82ouxwth183vc8WdbV1UX//v3RpUsXZGRkwMDA4Lv/Pyqi77l+zM3NMWHCBKSnp8PW1pYVM5Sh9PR0BAYG8g9zAUBbWxsLFy7kW0xi28KPIe3H3tLSEhs2bMC0adNw+vRpiMViLFmy5LM3zZjvQ3octrS0xMyZM9GyZUv4+PjgzZs3yM3NRZs2bdC7d290794dANsOfjQzMzNYWFjg9OnTOH78OEQiEUQiEVxcXPhiBnbD+Mdg+6Jv9+HDB/z11194+PAhzMzMYGJiUuRvNTs7Gzk5OVBRUSk0TF1dHTk5OQgPD0dMTAyaNm0KoPDvnm0DRWMZCI9lIDyWQfnAchAey6Bi0NfXh729Pfr3749///0XmZmZqFWrFqpXrw5DQ0MA7BrtR2MZMEz5wbYyhiklIuIfpK9fvx4rVqxAYmIiWrVqhYYNG6JmzZp8s9UA0LFjR8yZMwe1a9dGYGAgvL29kZiYKDc9WexksHikJwvu7u5Yt24d3r59iwYNGkAikWDbtm3YunUrHj58CAAwMDDAn3/+idGjRyM9PR2bN2/G7NmzMXv2bLi6uuLBgwf8dA8ePMiKGT4jOjoaSUlJUFBQQG5uLv+5tHWLkvj0d862g+L5nhnIIiJoamryxQysoOTzflQGlpaW2LhxI/8mNGtIq2xoaWlh3LhxWLNmDQYPHgxXV1ds2bKFFTOUEemDRAsLC2zYsAHVqlXDuXPncO/ePaFnrcKTrnsAqF69Ovr27Yt9+/bh1KlTOHPmDDZs2MCKGcqQpaUlJk+ejMGDB6Nu3bro27cvNmzYgGHDhgEoyICdG/04bF/0bapUqQJHR0c4OjqiZ8+eEIlEeP/+PT9cVVUV9erVQ0xMDK5du8YX7MhSU1ODmZkZgIImrqXY7754WAbCYxkIj2VQPrAchMcyqDhEIhE0NTXRo0cP2NnZoUWLFvyDdHbfrmywDBimfGAtNDBMKUlP3v7++2/4+Pigffv2mDJlCkxNTfHu3Tvo6ekBANLS0qCoqAgNDQ3Y2tqCiLBmzRrs378fYrEY48ePxy+//MJOBktItnWM27dvY+/evWjTpg2mTp0Kc3NznD59Gr6+vjhx4gQkEglGjRoFMzMzGBgYwM7ODsrKyvDw8ODfxp07dy5/kv73339j3bp1UFBQYMUMRTh69CjOnj2LXbt2oVatWvznCgoK3/Swgz0oKb4flQGrsi++H5EBEYGIULVqVf7fLIMfT7qeDQ0NYWhoWKhJd3ZxWjZkHyR6eHjgyZMnaNeundCz9Z/w6X5GS0uL/0xa7MC2gx9Pui9q164d2rVrhxkzZsj1d8zOk8oG2xeVjvT326tXL3Tr1g1KSkpYt24d4uLi4OTkhAYNGqBKlSro2rUrwsLCsGTJElSuXBmNGzcGUHBtp6CgIFdkJb2eZoqHZSA8loHwWAblA8tBeCyDiuVL1wDsnlHZYBkwTPnA7ogwTCkREVJSUnD06FFUrlyZL2YACk7yrl27hjVr1qBHjx4YO3Ys1q9fDwDo3LkzXFxcYGBggAMHDiA+Pl7IxfhpSYsZXr9+jdzcXGRnZ2Ps2LEwNzcHAHTv3h3Tp0+HlZUVTp06he3bt/MtMOjr62Pw4MHYsmULpkyZAm9vbwwfPhwAkJmZiYSEBGRlZWHXrl2smKEIubm5ePXqFRwdHZGVlYVly5bhzz///KaHHbI36WWrvpmisQyE9yMykP1uXFwcuygqI5+uZ7FYzP/NikrKlvSGmZWVFf744w8AQH5+vsBz9d9TVGEb2w5+vKLeiGMFJcJg+6KSE4lE/DpSUlLCixcvcPz4cZw7dw67du1CdHQ0AGDo0KHo378/3rx5g8WLF+PmzZsACq7tRCIRX6hetWpV1KxZU7Dl+RmxDITHMhAey6B8YDkIj2VQMX3aIui3thDKlBzLgGGEJSLWljHDlNrbt2/Rr18/cByH7du3AwCeP3+OI0eOwNvbm38QoqKigpycHMycOROjR48GAJw9exaZmZno37+/kIvwU1u+fDkCAgLQqFEjpKWl4fjx4wAKbjgqKRU0QBMcHAwPDw/cvXsXPXr04FtqKIo0rzdv3gAoaHqZKSD7UC8nJwfjx4/HjRs3oK2tjbS0NLRq1QqLFi1CnTp1Sjxt2Qfp+/btg4eHBxYsWIAePXp812X42bEMhMcyqPiKegOaFTWULdkWmKRYBmWLbQfCYxkIj+2LSkZ23Tx8+BCmpqY4f/48vL29ERkZif79+2P48OHgOA5v3rzBqlWrcOrUKSgpKWHy5MmoWbMmMjIysHfvXsTGxsLV1RX29vYCL9XPhWUgPJaB8FgG5QPLQXgsg/JFem7/LeeSstcHkZGR/At9TPGwDBimYmAFDQzzDdLT0/Hnn3/i/fv3GDt2LN69e4eQkBBERUVBJBJhxowZMDMzQ3JyMubOnYtOnTrB3d290HRYE7Kl4+3tDXd3d6iqqkJfXx++vr6oU6cO/zab9ARFtqihV69eGDly5GeLGpjCQkJCcOLECbi4uKBSpUr8561bt0ZaWhqUlJSwefNmNG/eXK6YpDhkf/v//PMPNm7ciHfv3uHIkSOsdQwZLAPhsQzKny8dO0tzXJX9ztGjR6GpqYlOnTp983xWZCwD4bEMhMcyEB7LoPzYtGkTjh8/jvPnzwMAzp8/jy1btuDhw4fo168fRo8eDUNDQ2RkZMDLywt+fn5y369atSomTZqEIUOGAGBFJKXBMhAey0B4LIPygeUgPJaBMD49/5QWykrXX1GFs8Wd3oEDB7B9+3b06tULkydP/u7zXlGwDBimYir+3XaG+Q/73I0wLS0tDBs2DOvXr4ebmxsAQEdHB3379kX//v3RtGlTAEBKSgq0tLQQFRWF3NxcKCkpyU2PFTOUzrhx41CpUiWsWLECcXFxOHPmDMaOHQuRSMQ3+SQSidCqVSsAwObNm3HixAlkZGRg5cqV0NXVFXL2fwofPnyAi4sLEhISYGpqikGDBkEikeDSpUt49+4dtLS0kJ6ejkWLFsHPzw81a9Ys9knhpw9x3dzckJubi6NHj8LIyOhHL9pPg2UgPJZB+SO7fq9evYpXr15BVVUVRkZGMDMzg4KCQokeYn2aw9q1a6Guro5mzZpBQ0OD3bQpAstAeCwD4bEMhMcyKD/S09MRERGB+Ph4BAQEwN7eHp07d4aCggK8vLxw+PBhAMCoUaNQv359zJkzB23btkVSUhIePXoEKysr1K1bly88Z0X/JccyEB7LQHgsg/KB5SA8loEwZM9Nr1y5gnv37iE8PBwGBgaoWbMmBg8eDD09vWJPr6h7Rjk5OejWrdsPmf+KgGXAMBUYMQzzRfn5+fzfUVFRdOPGDTp27Bilp6cTEdGHDx/o33//JVdXV1q2bBk9fPiQUlJSiIhIIpEQEVFQUBCZmZnRihUrynz+KwLpepT+l4goLy+P/3vPnj3EcRxxHEd79+7lPxeLxXLf+ffff6lXr17k5+f342e6gsjNzaUTJ07QwoUL6cOHD0RUsE0kJibS/v376dGjRzRy5EjiOI5sbW0pISGBH+dLxGIx//eBAweoRYsWZGNjQ1FRUT9uYX5SLAPhsQzKF9n9+saNG/n9P8dx1KtXLzp06BA/XHYdf86nObRs2ZIaN25Mjx49+r4zXoGwDITHMhAey0B4LIPy5+bNm8RxHA0dOpRev37Nf37hwgXq168fcRxHc+fO/eo6lc2WKRmWgfBYBsJjGZQPLAfhsQzKluy5pJubG5mamsqdn3IcRx06dKDLly9TTk5OiaYnvWfUuHFjds/oC1gGDFOxsYIGhvkC2YOWr68vdezYkYyNjYnjOJozZw5f1EBUcHInfXj18eNH/vPQ0FCys7MjCwsLCgoKKruZryBkHwi+f/+eYmNj6dmzZ/TixQu58Xbv3l2sogbpg0YidkJeXPn5+XwByV9//UXOzs6UnZ3ND8/NzSUHB4dCD3Nzc3OLnB57iFtyLAPhsQzKn/379/MPrVauXEljxowhjuOoadOmFBgYyI/3pYdYLIdvwzIQHstAeCwD4bEMygfp9fDMmTPJ1NSUrl69Kjf804cnjx8/5od9rQiUKR6WgfBYBsJjGZQPLAfhsQyEs3nzZuI4joYNG0ZXrlyhmJgYCgsLo/HjxxPHcdSiRQs6f/48EX3+3jQ7N/02LAOGqZhYQQPDfIbswWzdunXEcRx17NiR1qxZQ05OTnTixAnKysoq9L3Q0FCaPn06bdy4kby9valdu3bEcRzt2rWrLGe/QpA9cdi1axf179+fOI4jU1NTatiwIa1Zs4bCwsL4cYrbUgMRK2YojdjYWLK0tCSO42jRokVyhTt5eXk0dOhQ/mHuq1evipwGOxn8NiwD4bEMhCPdb0vXn7OzM3Xv3p2io6OJqKBgbdOmTcV+iMVyKDmWgfBYBsJjGQiPZSCsoh5yyF5bHT58mDiOo4EDB9K7d+/kxpM+PDEzM6PZs2ez1i9KiWUgPJaB8FgG5QPLQXgsg/IlPDycmjdvTj179izyXPKvv/4ijuOoVatWfBHJp+en7Nz027AMGKbiYgUNDPMVhw4dIhMTExozZgw9fPiQiEiuSaKkpCR6//49paWlEVHBQVHaioP04Lh//35+/OI0d8rIn3yvXbuWX5ezZ88mZ2dnatq0KXEcR3/88QcdPHiQHzcgIIBf97Lrnfl2OTk5dOHCBerUqRNxHEeurq5ffJj79u1bunHjBi1evJgePXokd5HFTgZLh2UgPJaBMGTXW35+Pn38+JF69OhBXl5ecuOlpKSQh4fHVx9isYvTkmMZCI9lIDyWgfBYBuXHrVu3KC4ujv+3bDbjxo2jRo0aUUhICBHJXz9funSJevXqRRzHUXBwcNnNcAXEMhAey0B4LIPygeUgPJZB+XD8+HEyMTEhf39/uc+lLX7m5+eTi4sLcRxHw4cPl7ufRMTOTb8HlgHDVFxKYBjms8RiMc6dOwc1NTVMmTIFJiYmICKoqKjg4sWLuHDhAs6ePQsdHR00bNgQLi4umDx5Mtq3b4+wsDD89ttvqF27NiwsLAAAEokECgoKAi/Vz0EkEgEAjhw5Al9fX7Ru3RqzZ8+GkZERAODFixeYNGkSHj16hMjISHTo0AE6OjoYMmQIiAjLli3DokWLkJWVhREjRgi4JD8nIuIzkFJRUUHr1q0xb948LF++HIGBgQCAuXPnQl1dHUpKStixYwccHR0REhKCPn36QCwWIzU1FU2aNIGxsTEA4J9//oGbmxtyc3MREBAAjuPKfPl+BiwD4bEMygeJRAJFRUUAwKFDhxAaGopKlSohNTUVHz58gEQiARFBUVERVapUgYODAwDA09MT69atAwDY2dlBQUEBEokEAPhjMcuheFgGwmMZCI9lIDyWQfmxbt06+Pr6wtTUFB07doSjoyNUVFT44X369EFQUBB2796NJk2aQEVFBWKxGIqKiujQoQNycnKQn5+Pli1bCrgUPzeWgfBYBsJjGZQPLAfhsQzKjwcPHkAikUBNTQ0AkJ+fDyUlJSgpKfHrfMGCBYiIiEBsbCzevHmDunXrApB/bsDOTUuPZcAwFRcraGCYzyAiZGVlISYmBrVq1eIfQj148AAnT56En58fAKBy5crIycnBqVOnULVqVcyePRvNmzdH8+bNC02PFTMULTMzExoaGkUOu3btGlRUVDB+/Hi+mAEATp06hdjYWLRq1QojR46EWCxGTEwMGjRoAHt7exARli9fjpycnLJajApDenIHAGlpaUhJSYGamhqqVasGVVVVtG7dGgsWLPjsw1w/Pz+MHz8ed+7cgaKiIlxdXdGjRw8AwOnTp7FmzRoQETsZ/AKWgfBYBuWH9Njp5uaGbdu2yQ2LjY1FRkYGtLW1+YvUTx9iubu7QyKRYODAgXLHYXZxWnwsA+GxDITHMhAey6D8qF27Nn7//XecPn0aDx8+xKVLl9CxY0f0798fNWvWRMuWLWFqaopz587h33//Rdu2baGoqMgXi3br1o2fFiv6Lx2WgfBYBsJjGZQPLAfhsQzKj5o1awIAHj58CABQUlLi17OioiLy8/OhoqKCGjVq4OnTp0hMTOQfpkvX+4EDB7Bx40Z2blpKLAOGqcDKrjEIhvk5OTk5kbm5OS1ZsoTmzJlDXbt2JY7jyNLSkgICAigqKopCQkKoXbt29Pvvv/NNesl2mcB83q1bt2j06NEUGhpaaNiHDx+oQ4cO1LdvX7nPpc3HOjo6UnR0NCUkJNDvv/9OHh4ecuOxpqBKTrZJuj179tDAgQOJ4zhq3LgxbdmyhR+Wl5dHly5dog4dOhDHcbRgwQK5JrrEYjHFxMTQkydP5KYfFRVFv//+O+uX7wtYBsJjGZQPssfRY8eOkZmZGQ0dOpQCAgLIz8+PLC0tieM4mjFjBj+etAlBooLmxr28vPhuiKR9IxIV5Nq6dWvWbOBXsAyExzIQHstAeCwDYX3uulYikdD9+/dp1qxZ/LlQ8+bNycfHh16+fEkRERHUsGFDWrlyZRnPccXDMhAey0B4LIPygeUgPJaB8L50z//hw4dkZmZGTZo0oTNnzsh9R/Z7kydPpqZNm9KLFy/kvn/t2jUyMzOjRo0aUXR09Pef+QqCZcAw/00iIiKhiyoYRmj0/1V6JNO8uFgshkgkwpkzZ+Dl5YUnT54AAKpUqYKePXuiV69esLa25qfRu3dvfPjwAadPn4ampqYgy/GzycjIwNSpUxEcHIypU6di3LhxclXAycnJ+OOPPyAWixEYGAgDAwN4enrC09MTrVq1wowZM2BqaoorV65g3LhxMDMzw969e6GkpCRXacyqi4tH9ve/du1abN++Hbq6ujA0NER+fj4GDBiAnj178s3W5eTk4Pr161i2bBkSEhJgZ2eHefPmQV1dvdC0JRIJRCIRRCIRcnJyoKqqWqbL9rNgGQiPZVA+fLrf9vLywp49e+Dn58e3mHTv3j0MGzYM2dnZ+P3337F69WoA/2tOEAA+fPgAX19f6Onp8d0PJScnY/jw4YiJicGxY8fkWv9h/odlIDyWgfBYBsJjGQhLtsUqIkJKSgoAQFdXlz9nSk9PR1paGrZv346goCAkJCRAT08Ptra2uHv3Lp4+fYodO3agcePGQi7KT4tlIDyWgfBYBuUDy0F4LAPhFZWBSCSCjo4On8HGjRuxbds2WFlZYdy4cWjbti0/vkgkwq1btzBhwgRYWlrC3d0dVapU4e9DPX36FJs2bcK4ceNgYmIi2HKWZywDhvnvYgUNzH+e7EEwMzMTKSkpUFVVhUgkgp6eHvLz8/HkyRPcuHED+fn56NKlC6pXr873wwQAN27cwNixY9GlSxesWbMGIpGIPUAvBiLC9evXERERAXt7e1SuXBmvX7+Gvr4+P86sWbNw/vx5bNmyBbdv3y5UzAAAr169Qu/evWFoaIiAgAC5fuKYkgsICMCKFSvQpk0bTJ8+HcbGxsjKyoKioiJUVFSQlZXFP6wlIly+fBnLly9HQkIC+vXrB1dXV1SqVOmz05d9YMwUjWUgPJZB2frc+liyZAmePXsGBQUF1KpVC0uXLoVEIoFYLIaysjIiIyPh4ODwxYdYubm5/HFB+mDs7t270NLSgqGhYdktZDnHMhAey0B4LAPhsQzKD9nr5MDAQAQFBeH27dtQVFSEpaUl2rdvj65du0JHR4f/TkxMDIKDg+Hr64vU1FTk5eVBJBJhxIgRmD59OrtOKyGWgfBYBsJjGZQPLAfhsQyEV5wMevXqhaysLKxYsQJnz56FsbEx+vXrhyFDhgAAbt26xd/jXrduHXr27Fno/yN7zsrIYxkwzH8bK2hg/tNkD4IBAQE4deoUIiIioKKigkqVKmH48OHo2LEjf4NLekMsMzMTKioqUFZWRkhICDZu3Ig7d+5g06ZNsLW1FXKRfgpxcXEQiUSoXbs2iAhisRhKSkpYs2YNwsLCsGDBAlhaWgIA9u7di6VLl6JSpUr4+PEj2rdvj0mTJsHCwoKf3qlTp+Ds7IyRI0dizpw5Qi3WTyU2NhZEhAYNGsh9npycjFGjRiE1NRUeHh4wMzPjh926dQshISG4fPkyKleuDHt7e7Ru3RpKSkq4du0aFixYgHfv3sHPzw8tWrQo60X66bAMhMcyKB+ePHmCu3fvon///pBIJHwLOxkZGejfvz/i4+OhpKQEKysreHl5oXLlygD+dwz/3EOsvLw8KCsrC7loPw2WgfBYBsJjGQiPZVB+yBaWrFu3Dr6+vtDU1ISpqSmSkpLw7t075OTkoHXr1li2bBmqV68u9/2YmBg8ePAAO3bswOPHj7F06VIMGDBAiEX5abEMhMcyEB7LoHxgOQiPZSC84mSQlZWFdu3aYdWqVUhLS4OPjw8OHjwIiUSCWrVqQSwWIzExEQDg4uLCtxrGXngpHpYBwzD4Lh1XMMxPSLbPpLVr1xLHcdS0aVOaOHEiOTo6kpmZGRkbG9Pw4cPp4sWL/LihoaHUr18/cnZ2psWLF1OTJk2I4zjy8/MTYCl+PhEREWRlZUULFy6k+Ph4/vP4+Hj6448/iOM4Gjt2LEVERPDDnJyciOM4srKyosOHD8tNT5pHo0aN6MqVK2W1GD+1R48eEcdxNGDAAEpISJAb9vTpU+I4jubPn09ERPn5+RQfH0+enp5kZmbG9z3McRy1a9eOwsPDiYgoOzubzp49SwcOHCjz5fkZsQyExzIoP4KCgsjU1FTuOCrtx/zly5c0YMAA4jiOOnfuTLdu3ZL7bn5+PhER3b9/nxo2bEgcx5Gzs3OZzXtFwTIQHstAeCwD4bEMhCUWiwt9FhAQQBzH0ejRo/nrs7dv39KNGzeoadOmZGZmRqtWreLXv/S/UpmZmRQaGvrjZ76CYBkIj2UgPJZB+cByEB7LoHz6Wgampqa0Zs0aIiJKTk6ms2fPkp2dHXXv3p3atWtHs2fPpgsXLvDTKypn5stYBgzz38UKGpj/vMDAQOI4jsaMGcPfMCMqePDesWNHsrCwoFWrVlF6ejpJJBLy9/endu3aEcdx1LBhQ7K1tZV7eMUOgl926NAhat26NdnY2NDKlSvp5cuXRFSw3kJDQ2nMmDHEcRyNGjWK7ty5w3/P0dGROI6jli1b0qpVq2jPnj3k4eFBrVu3Jo7jaPfu3QIt0c/n+vXr1LFjR5o8eTJ9/PhRbtjbt2+pXbt21KVLFwoODiY3NzcaPHgw//DW09OTgoKCaOXKlcRxHM2cOZP/rmyRENsOvoxlIDyWQflx/vx5ft0eOHCAFi5cSBzH8YUiCQkJZGdnxxegPHv2TO770ps0kZGRZGpqShzH0dWrV8t6MX5qLAPhsQyExzIQHstAGI8fP6Y3b94Q0f/OXSQSCWVmZtKQIUOoadOm9PDhQ7nvbNy4kb+GfvHiBaWkpFBmZqbcOJ8+RGHnRZ/HMhAey0B4LIPygeUgPJZB+VSSDJ49e0YfPnygjIwMIiLKyMig3Nxc/tmCFMugZFgGDMOwggbmPy0vL48mTpxIVlZW/MNz6UHN09OTOI4jR0dHiouLo/fv39P79+8pOzubYmNj6fjx4xQSEkKPHz/mp8cOgl+Xk5NDhw8fpq5du1LDhg1p5cqVFBcXR0QF6z40NJQvXhg1ahR/A5OIaPHixdShQwe5t6M7depEgYGB/Dgsg6/Lz8+n6OhoSk9PJyKiw4cP8yeBeXl5NHPmTLl13LhxY5oyZQpdv36dn0ZaWhqZm5vThAkT5E4EmeJhGQiPZVC+7N27V259Ozo6UmxsLD88ISGBfzN38ODB/HFDSrrvv3v3Lu3Zs6dM572iYBkIj2UgPJaB8FgGZevevXv8ev704UlcXBxxHEdTp04lov9dJ3t4ePDfiYmJoZcvX9KQIUPo9OnTcuMxxcMyEB7LQHgsg/KB5SA8lkH5VtIMTp06RUQF95ik2H3rb8MyYJj/NgWhu7xgGCF9+PABYWFhsLCwgJWVFQBAJBLB09MTHh4eaNWqFebOnQsFBQWMGzcOZ86cgaqqKgwNDdGrVy80adKE73udiKCgwDapLyEiqKiooEePHhg3bhxq1KiBv//+G3v27EF8fDxEIhFsbGwwYcIEtGrVCteuXYOXlxfu3LkDAFi0aBF8fHzg5uaGhQsXYseOHdi+fTvs7OwAABKJhGXwFUQERUVFGBkZQVNTE0eOHIGLiws8PDwQFRUFJSUlrFixAuPHj8eff/6JLl26YNu2bXB1dUWLFi346dy8eRN5eXkwMTGBSCQCEQm4VD8XloHwWAblR35+PgBg8ODBsLCwgJKSEgDA3NwchoaG/Dg1atTAxo0b0bBhQ4SHh2POnDmIj4/np6OgoACxWIyGDRvC3t4eQEGf6szXsQyExzIQHstAeCwDYeTk5KB27doIDg7G0qVL8fbtWygoKPD9CItEImRnZwP433Wyp6cnWrVqBWdnZ9SvXx+XLl3C7du3cffuXX48pvhYBsJjGQiPZVA+sByExzIoPz49f5RIJCXOICIiAgD481oA7L51CbAMGIb5FNt6mf+0vLw8iMVivH//Hh8+fACAIg+CoaGhuH//Pi5evMjfbPsUO0H8OukDP9mihurVq2P//v1fLWqQnoAYGhqiR48eGDJkCFq2bIm6desCYAUlxSX7Oz1+/DgAoGnTprh8+TI8PT3x6NEjqKioYOrUqVi+fDnc3d1hbW0NPT09/nu3b9+Gj48P1NXV0aRJk0LTZb6MZSA8lkH5IBaL+YvKZ8+e4dWrV6hevToAwNvbG/7+/iAiKCkpQSwWw8DA4IsPsRQVFeWm/+m/mcJYBsJjGQiPZSA8loFwrK2tsWrVKhgbG+P8+fP8wxORSAQVFRVoaWnh4cOHiImJkbtOnjFjBszMzAAAv/32GwDw19OswLNkWAbCYxkIj2VQPrAchMcyKD+k548BAQF4+fIlFBQUWAZljGXAMEwhZdIOBMMI7EtNCQ0fPpyaNWtGjx49kmue6MGDB/w40dHRZGJiQo6OjoX6HGOK53MZHDp0iPr06UMWFhZf7X7i/v37/PdYk2nfxs/PjziOoytXrlBYWBgNHDiQOI6jSZMmUVRUlNy4169fp3HjxtHx48fJx8eHOnbsSBzHkb+/v0BzXzGwDITHMigfPDw8yNPTk6KjoyktLY327dvHNzW+a9cufjzp8TcxMZFvbvzPP/+k58+fCzXrFQbLQHgsA+GxDITHMhCGRCKhkJAQ+vPPP4njOJo4cSIlJCQQEdGaNWuI4zjq2bMnf032aX/F0pwCAgL46TElwzIQHstAeCyD8oHlIDyWgbCk968lEgkdP36cOI6jfv368ferV69ezTL4wVgGDMN8DnudmanwxGIx/+b+gwcPcPbsWRw7dgwhISEAgBYtWuDDhw8YPXo0PD090b59e0ybNg2mpqb8NB4+fAiJRAJra2v2hk8pyGZw+vRpLF++HAMGDICrqysAYMCAAahVq9YXu58IDg7GunXrWJNp34FEIoGqqioA4OLFi7CxscHMmTPRqFEjXLhwAR4eHnj8+DEAIDc3F//88w+CgoIwc+ZMrFu3DhkZGVi0aBGGDh3KT48pGZaB8FgG5UNISAg8PT3h5+eHW7duQUtLC4MGDcKcOXMAACtXroS/vz+A/zUjLn0z19TUFPfv38fDhw+FXISfHstAeCwD4bEMhMcyKHvSZnxFIhFq164Ne3t7aGtrIzg4GMuXL0dycjL69+8PjuPw5MkT1KxZEwMHDoSJiQk/jbCwMAQEBKBy5cr85+w6rfhYBsJjGQiPZVA+sByExzIQnuz96zdv3qB+/frQ09PDo0ePMH36dLx58wZ2dnYwMjJiGfwgLAOGYb5ERMTaW2EqLolEwh8Et23bht27d+Pt27cAgHbt2mH9+vUAgLFjxyI8PByVK1fGX3/9hY4dO/LTCAsLw7Jly5CYmIgNGzagZcuWZb8gPzH6/37eAMDd3R3e3t4AABUVFeTm5qJ9+/ZYunQprl+/jq1btyIpKQkDBw6Eg4MDateuDSJCeHg4Nm3ahFu3bsHT0xO2trZCLlKFEB8fj2HDhiExMRE7d+5Es2bNcPPmTXh5eSEsLAy2traYMmUKjIyMkJGRgZMnT+Lt27fQ19dH/fr1YW1tDUB+G2NKhmUgPJaB8NLS0rB//35s2rQJ6urqcHJywrBhwwAAO3fuxKpVqwAAc+fOxfDhwwEA79+/R9WqVfH69Wvcvn0bPXr0EGz+KwKWgfBYBsJjGQiPZVC2ZM9dtmzZgjNnziAuLg5ZWVn8OF26dMGqVatw+/ZtLFmyBC9fvoStrS1atmwJIyMjREdHY9euXYiLi8OSJUswcOBAoRbnp8QyEB7LQHgsg/KB5SA8loHwZDPw9PTE/v37kZ2djczMTIhEIkgkElhZWWHbtm2IjIzEwoULWQbfGcuAYZivYQUNzH+C9EH6r7/+iq5du0IikeC3336Dra0ttLS08OHDB4wcORKPHj2CoaEhevfujdq1ayMxMREBAQFITEyEq6sr7O3thV6Un5a/vz9WrlyJNm3aYOLEiahZsyZiYmJQt25d1K5dG7m5uTh58iR8fX0RHx+PwYMHyxU13Lp1CxkZGay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      "text/plain": [
       "<Figure size 2500x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.matrix(df_sessions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4a0cd110",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2500x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.matrix(df_hits)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8eb35d7a",
   "metadata": {},
   "source": [
    "Нам надо высчитать процент пропущенных значений в df_sessions и df_hits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8593fd01",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Процент пропущенных значений:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "session_id                   0.000000\n",
       "device_browser               0.000000\n",
       "device_screen_resolution     0.000000\n",
       "device_category              0.000000\n",
       "geo_country                  0.000000\n",
       "utm_medium                   0.000000\n",
       "geo_city                     0.000000\n",
       "visit_number                 0.000000\n",
       "visit_time                   0.000000\n",
       "visit_date                   0.000000\n",
       "client_id                    0.000000\n",
       "utm_source                   0.005215\n",
       "utm_campaign                11.806346\n",
       "utm_adcontent               18.043410\n",
       "device_brand                19.740307\n",
       "device_os                   57.533002\n",
       "utm_keyword                 58.174009\n",
       "device_model                99.121633\n",
       "dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_values_ses = ((df_sessions.isna().sum() / len(df_sessions)) * 100).sort_values()\n",
    "print('Процент пропущенных значений:')\n",
    "missing_values_ses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2282ca95",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Процент пропущенных значений:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "session_id          0.000000\n",
       "hit_date            0.000000\n",
       "hit_number          0.000000\n",
       "hit_type            0.000000\n",
       "hit_page_path       0.000000\n",
       "event_category      0.000000\n",
       "event_action        0.000000\n",
       "event_label        23.909905\n",
       "hit_referer        39.899634\n",
       "event_value       100.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_values_hit = ((df_hits.isna().sum() / len(df_hits)) * 100).sort_values()\n",
    "print('Процент пропущенных значений:')\n",
    "missing_values_hit"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c652e6a6",
   "metadata": {},
   "source": [
    "## Feature engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af616c6c",
   "metadata": {},
   "source": [
    "### Выделение месяца"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "832145bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sessions['month'] = df_sessions.visit_date.dt.month"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3702145d",
   "metadata": {},
   "source": [
    "### Разделение целевых действий"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a2f5ac9",
   "metadata": {},
   "source": [
    "Далее мы выбираем только те строки, где были совершены целевые действия, после мы создаем признаки по целевым действиям и проверим количество уникальных значений в столбце target_action"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "dcc2e8b9",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>session_id</th>\n",
       "      <th>hit_date</th>\n",
       "      <th>hit_number</th>\n",
       "      <th>hit_type</th>\n",
       "      <th>hit_referer</th>\n",
       "      <th>hit_page_path</th>\n",
       "      <th>event_category</th>\n",
       "      <th>event_action</th>\n",
       "      <th>event_label</th>\n",
       "      <th>event_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4016</th>\n",
       "      <td>2744563715298057088.1640258436.1640258436</td>\n",
       "      <td>2021-12-23 00:00:00+00:00</td>\n",
       "      <td>81</td>\n",
       "      <td>event</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sberauto.com/cars/all/kia/rio/fee33fe6?utm_sou...</td>\n",
       "      <td>sub_submit</td>\n",
       "      <td>sub_submit_success</td>\n",
       "      <td>nsPPIRqjxBefONGPpnsF</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4045</th>\n",
       "      <td>3087297479839089634.1640268774.1640268774</td>\n",
       "      <td>2021-12-23 00:00:00+00:00</td>\n",
       "      <td>22</td>\n",
       "      <td>event</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sberauto.com/cars/all/skoda/rapid/bf24b977?utm...</td>\n",
       "      <td>sub_submit</td>\n",
       "      <td>sub_submit_success</td>\n",
       "      <td>nsPPIRqjxBefONGPpnsF</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4046</th>\n",
       "      <td>3156966333326004302.1640206419.1640206800</td>\n",
       "      <td>2021-12-23 00:00:00+00:00</td>\n",
       "      <td>63</td>\n",
       "      <td>event</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sberauto.com/cars/all/skoda/rapid/bf24b977?utm...</td>\n",
       "      <td>sub_submit</td>\n",
       "      <td>sub_submit_success</td>\n",
       "      <td>nsPPIRqjxBefONGPpnsF</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4047</th>\n",
       "      <td>3750243879753098158.1640272208.1640272208</td>\n",
       "      <td>2021-12-23 00:00:00+00:00</td>\n",
       "      <td>20</td>\n",
       "      <td>event</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sberauto.com/cars/all/nissan/x-trail/0744675f?...</td>\n",
       "      <td>sub_submit</td>\n",
       "      <td>sub_submit_success</td>\n",
       "      <td>nsPPIRqjxBefONGPpnsF</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4048</th>\n",
       "      <td>7518333712042258254.1640258901.1640258901</td>\n",
       "      <td>2021-12-23 00:00:00+00:00</td>\n",
       "      <td>16</td>\n",
       "      <td>event</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sberauto.com/cars/all/mercedes-benz/gla-klasse...</td>\n",
       "      <td>sub_submit</td>\n",
       "      <td>sub_submit_success</td>\n",
       "      <td>KuMiABMMbspIDDhiCNVS</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15725025</th>\n",
       "      <td>1277864870843199549.1636773954.1636773954</td>\n",
       "      <td>2021-11-13 00:00:00+00:00</td>\n",
       "      <td>30</td>\n",
       "      <td>event</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sberauto.com/cars/all/lada-vaz/vesta/2fc745ed?...</td>\n",
       "      <td>sub_button_click</td>\n",
       "      <td>sub_car_claim_submit_click</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15725133</th>\n",
       "      <td>965861352491898977.1636751459.1636751459</td>\n",
       "      <td>2021-11-13 00:00:00+00:00</td>\n",
       "      <td>18</td>\n",
       "      <td>event</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sberauto.com/cars/all/volkswagen/polo/e994838f...</td>\n",
       "      <td>sub_submit</td>\n",
       "      <td>sub_submit_success</td>\n",
       "      <td>nsPPIRqjxBefONGPpnsF</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15725134</th>\n",
       "      <td>4727705172767122620.1636818847.1636818847</td>\n",
       "      <td>2021-11-13 00:00:00+00:00</td>\n",
       "      <td>43</td>\n",
       "      <td>event</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sberauto.com/cars/all/volkswagen/polo/e994838f...</td>\n",
       "      <td>sub_submit</td>\n",
       "      <td>sub_submit_success</td>\n",
       "      <td>uimgZZmhfLQwbKAZZfCk</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15725135</th>\n",
       "      <td>4727705172767122620.1636818847.1636818847</td>\n",
       "      <td>2021-11-13 00:00:00+00:00</td>\n",
       "      <td>41</td>\n",
       "      <td>event</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sberauto.com/cars/all/volkswagen/polo/e994838f...</td>\n",
       "      <td>sub_button_click</td>\n",
       "      <td>sub_open_dialog_click</td>\n",
       "      <td>ZaZuwAXOKlbzyhUqtnmk</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15725136</th>\n",
       "      <td>116562085450617659.1636832061.1636832061</td>\n",
       "      <td>2021-11-13 00:00:00+00:00</td>\n",
       "      <td>26</td>\n",
       "      <td>event</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sberauto.com/cars/all/volkswagen/polo/e994838f...</td>\n",
       "      <td>sub_button_click</td>\n",
       "      <td>sub_car_claim_submit_click</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>104908 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         session_id                  hit_date  \\\n",
       "4016      2744563715298057088.1640258436.1640258436 2021-12-23 00:00:00+00:00   \n",
       "4045      3087297479839089634.1640268774.1640268774 2021-12-23 00:00:00+00:00   \n",
       "4046      3156966333326004302.1640206419.1640206800 2021-12-23 00:00:00+00:00   \n",
       "4047      3750243879753098158.1640272208.1640272208 2021-12-23 00:00:00+00:00   \n",
       "4048      7518333712042258254.1640258901.1640258901 2021-12-23 00:00:00+00:00   \n",
       "...                                             ...                       ...   \n",
       "15725025  1277864870843199549.1636773954.1636773954 2021-11-13 00:00:00+00:00   \n",
       "15725133   965861352491898977.1636751459.1636751459 2021-11-13 00:00:00+00:00   \n",
       "15725134  4727705172767122620.1636818847.1636818847 2021-11-13 00:00:00+00:00   \n",
       "15725135  4727705172767122620.1636818847.1636818847 2021-11-13 00:00:00+00:00   \n",
       "15725136   116562085450617659.1636832061.1636832061 2021-11-13 00:00:00+00:00   \n",
       "\n",
       "          hit_number hit_type hit_referer  \\\n",
       "4016              81    event         NaN   \n",
       "4045              22    event         NaN   \n",
       "4046              63    event         NaN   \n",
       "4047              20    event         NaN   \n",
       "4048              16    event         NaN   \n",
       "...              ...      ...         ...   \n",
       "15725025          30    event         NaN   \n",
       "15725133          18    event         NaN   \n",
       "15725134          43    event         NaN   \n",
       "15725135          41    event         NaN   \n",
       "15725136          26    event         NaN   \n",
       "\n",
       "                                              hit_page_path    event_category  \\\n",
       "4016      sberauto.com/cars/all/kia/rio/fee33fe6?utm_sou...        sub_submit   \n",
       "4045      sberauto.com/cars/all/skoda/rapid/bf24b977?utm...        sub_submit   \n",
       "4046      sberauto.com/cars/all/skoda/rapid/bf24b977?utm...        sub_submit   \n",
       "4047      sberauto.com/cars/all/nissan/x-trail/0744675f?...        sub_submit   \n",
       "4048      sberauto.com/cars/all/mercedes-benz/gla-klasse...        sub_submit   \n",
       "...                                                     ...               ...   \n",
       "15725025  sberauto.com/cars/all/lada-vaz/vesta/2fc745ed?...  sub_button_click   \n",
       "15725133  sberauto.com/cars/all/volkswagen/polo/e994838f...        sub_submit   \n",
       "15725134  sberauto.com/cars/all/volkswagen/polo/e994838f...        sub_submit   \n",
       "15725135  sberauto.com/cars/all/volkswagen/polo/e994838f...  sub_button_click   \n",
       "15725136  sberauto.com/cars/all/volkswagen/polo/e994838f...  sub_button_click   \n",
       "\n",
       "                        event_action           event_label  event_value  \n",
       "4016              sub_submit_success  nsPPIRqjxBefONGPpnsF          NaN  \n",
       "4045              sub_submit_success  nsPPIRqjxBefONGPpnsF          NaN  \n",
       "4046              sub_submit_success  nsPPIRqjxBefONGPpnsF          NaN  \n",
       "4047              sub_submit_success  nsPPIRqjxBefONGPpnsF          NaN  \n",
       "4048              sub_submit_success  KuMiABMMbspIDDhiCNVS          NaN  \n",
       "...                              ...                   ...          ...  \n",
       "15725025  sub_car_claim_submit_click                   NaN          NaN  \n",
       "15725133          sub_submit_success  nsPPIRqjxBefONGPpnsF          NaN  \n",
       "15725134          sub_submit_success  uimgZZmhfLQwbKAZZfCk          NaN  \n",
       "15725135       sub_open_dialog_click  ZaZuwAXOKlbzyhUqtnmk          NaN  \n",
       "15725136  sub_car_claim_submit_click                   NaN          NaN  \n",
       "\n",
       "[104908 rows x 10 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hits_event_action = df_hits[(df_hits['event_action'] == 'sub_car_claim_click') |\n",
    "                               (df_hits['event_action'] == 'sub_car_claim_submit_click') |\n",
    "                               (df_hits['event_action'] == 'sub_open_dialog_click') |\n",
    "                               (df_hits['event_action'] == 'sub_custom_question_submit_click') |\n",
    "                               (df_hits['event_action'] == 'sub_call_number_click') |\n",
    "                               (df_hits['event_action'] == 'sub_callback_submit_click') |\n",
    "                               (df_hits['event_action'] == 'sub_submit_success') |\n",
    "                               (df_hits['event_action'] == 'sub_car_request_submit_click')]\n",
    "df_hits_event_action"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "afeadc97",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_hits['target_action'] = df_hits_event_action.apply(lambda x: 1, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b2bd076f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_hits.target_action = df_hits.target_action.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "79ee44b8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    15621562\n",
       "1.0      104908\n",
       "Name: target_action, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hits['target_action'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1e4b220e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0           0\n",
       "1           0\n",
       "2           0\n",
       "3           0\n",
       "4           0\n",
       "           ..\n",
       "15726465    0\n",
       "15726466    0\n",
       "15726467    0\n",
       "15726468    0\n",
       "15726469    0\n",
       "Name: target_action, Length: 15726470, dtype: int32"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hits.target_action.astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "695af27f",
   "metadata": {},
   "source": [
    "### Разделение органического и платного трафика"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc258496",
   "metadata": {},
   "source": [
    "Здесь мы произведем разделение органических (organic) и неорганических (non_organic) значений и подсчитаем их"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "42eb0e2f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>session_id</th>\n",
       "      <th>client_id</th>\n",
       "      <th>visit_date</th>\n",
       "      <th>visit_time</th>\n",
       "      <th>visit_number</th>\n",
       "      <th>utm_source</th>\n",
       "      <th>utm_medium</th>\n",
       "      <th>utm_campaign</th>\n",
       "      <th>utm_adcontent</th>\n",
       "      <th>utm_keyword</th>\n",
       "      <th>device_category</th>\n",
       "      <th>device_os</th>\n",
       "      <th>device_brand</th>\n",
       "      <th>device_model</th>\n",
       "      <th>device_screen_resolution</th>\n",
       "      <th>device_browser</th>\n",
       "      <th>geo_country</th>\n",
       "      <th>geo_city</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9055447192389856083.1622453074.1622453074</td>\n",
       "      <td>2108385598.162245</td>\n",
       "      <td>2021-05-31</td>\n",
       "      <td>12:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>kjsLglQLzykiRbcDiGcD</td>\n",
       "      <td>organic</td>\n",
       "      <td>LTuZkdKfxRGVceoWkVyg</td>\n",
       "      <td>JNHcPlZPxEMWDnRiyoBf</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mobile</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Apple</td>\n",
       "      <td>NaN</td>\n",
       "      <td>375x812</td>\n",
       "      <td>Safari</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>9055469620715506713.1628883994.1628883994</td>\n",
       "      <td>2108390820.162888</td>\n",
       "      <td>2021-08-13</td>\n",
       "      <td>22:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>fDLlAcSmythWSCVMvqvL</td>\n",
       "      <td>(none)</td>\n",
       "      <td>LTuZkdKfxRGVceoWkVyg</td>\n",
       "      <td>JNHcPlZPxEMWDnRiyoBf</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mobile</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Samsung</td>\n",
       "      <td>NaN</td>\n",
       "      <td>412x869</td>\n",
       "      <td>Android Webview</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>9055469620715506713.1633110583.1633110583</td>\n",
       "      <td>2108390820.162888</td>\n",
       "      <td>2021-10-01</td>\n",
       "      <td>20:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>fDLlAcSmythWSCVMvqvL</td>\n",
       "      <td>(none)</td>\n",
       "      <td>LTuZkdKfxRGVceoWkVyg</td>\n",
       "      <td>JNHcPlZPxEMWDnRiyoBf</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mobile</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Samsung</td>\n",
       "      <td>NaN</td>\n",
       "      <td>412x869</td>\n",
       "      <td>Android Webview</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>9055469620715506713.1635878177.1635878177</td>\n",
       "      <td>2108390820.162888</td>\n",
       "      <td>2021-11-02</td>\n",
       "      <td>21:36:17</td>\n",
       "      <td>3</td>\n",
       "      <td>gVRrcxiDQubJiljoTbGm</td>\n",
       "      <td>referral</td>\n",
       "      <td>LTuZkdKfxRGVceoWkVyg</td>\n",
       "      <td>JNHcPlZPxEMWDnRiyoBf</td>\n",
       "      <td>puhZPIYqKXeFPaUviSjo</td>\n",
       "      <td>mobile</td>\n",
       "      <td>Android</td>\n",
       "      <td>Samsung</td>\n",
       "      <td>NaN</td>\n",
       "      <td>412x869</td>\n",
       "      <td>Android Webview</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Sochi</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>9055505230298952295.1638478433.1638478433</td>\n",
       "      <td>2108399111.163848</td>\n",
       "      <td>2021-12-02</td>\n",
       "      <td>23:53:53</td>\n",
       "      <td>1</td>\n",
       "      <td>fDLlAcSmythWSCVMvqvL</td>\n",
       "      <td>(none)</td>\n",
       "      <td>LTuZkdKfxRGVceoWkVyg</td>\n",
       "      <td>JNHcPlZPxEMWDnRiyoBf</td>\n",
       "      <td>puhZPIYqKXeFPaUviSjo</td>\n",
       "      <td>desktop</td>\n",
       "      <td>Windows</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1536x864</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Balashikha</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1860026</th>\n",
       "      <td>9055355469082180480.1636350848.1636350848</td>\n",
       "      <td>2108364242.163635</td>\n",
       "      <td>2021-11-08</td>\n",
       "      <td>08:54:08</td>\n",
       "      <td>1</td>\n",
       "      <td>fDLlAcSmythWSCVMvqvL</td>\n",
       "      <td>(none)</td>\n",
       "      <td>LTuZkdKfxRGVceoWkVyg</td>\n",
       "      <td>JNHcPlZPxEMWDnRiyoBf</td>\n",
       "      <td>puhZPIYqKXeFPaUviSjo</td>\n",
       "      <td>mobile</td>\n",
       "      <td>Android</td>\n",
       "      <td>Xiaomi</td>\n",
       "      <td>NaN</td>\n",
       "      <td>393x873</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Moscow</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1860029</th>\n",
       "      <td>9055376699099939975.1630766214.1630766214</td>\n",
       "      <td>2108369185.163077</td>\n",
       "      <td>2021-09-04</td>\n",
       "      <td>17:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>fDLlAcSmythWSCVMvqvL</td>\n",
       "      <td>(none)</td>\n",
       "      <td>LTuZkdKfxRGVceoWkVyg</td>\n",
       "      <td>JNHcPlZPxEMWDnRiyoBf</td>\n",
       "      <td>NaN</td>\n",
       "      <td>desktop</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1920x1080</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Khimki</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1860032</th>\n",
       "      <td>9055394269810294140.1629912447.1629912447</td>\n",
       "      <td>2108373276.162991</td>\n",
       "      <td>2021-08-25</td>\n",
       "      <td>20:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>bByPQxmDaMXgpHeypKSM</td>\n",
       "      <td>referral</td>\n",
       "      <td>LTuZkdKfxRGVceoWkVyg</td>\n",
       "      <td>JNHcPlZPxEMWDnRiyoBf</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mobile</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Samsung</td>\n",
       "      <td>NaN</td>\n",
       "      <td>360x800</td>\n",
       "      <td>Android Webview</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1860038</th>\n",
       "      <td>9055421130527858185.1622007305.1622007305</td>\n",
       "      <td>2108379530.162201</td>\n",
       "      <td>2021-05-26</td>\n",
       "      <td>08:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>fDLlAcSmythWSCVMvqvL</td>\n",
       "      <td>(none)</td>\n",
       "      <td>LTuZkdKfxRGVceoWkVyg</td>\n",
       "      <td>JNHcPlZPxEMWDnRiyoBf</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mobile</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Apple</td>\n",
       "      <td>NaN</td>\n",
       "      <td>390x844</td>\n",
       "      <td>Safari</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Stavropol</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1860039</th>\n",
       "      <td>9055422955903931195.1636979515.1636979515</td>\n",
       "      <td>2108379955.163697</td>\n",
       "      <td>2021-11-15</td>\n",
       "      <td>15:31:55</td>\n",
       "      <td>1</td>\n",
       "      <td>fDLlAcSmythWSCVMvqvL</td>\n",
       "      <td>(none)</td>\n",
       "      <td>LTuZkdKfxRGVceoWkVyg</td>\n",
       "      <td>JNHcPlZPxEMWDnRiyoBf</td>\n",
       "      <td>puhZPIYqKXeFPaUviSjo</td>\n",
       "      <td>mobile</td>\n",
       "      <td>iOS</td>\n",
       "      <td>Apple</td>\n",
       "      <td>NaN</td>\n",
       "      <td>375x667</td>\n",
       "      <td>Safari</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Moscow</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>515659 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        session_id          client_id  \\\n",
       "5        9055447192389856083.1622453074.1622453074  2108385598.162245   \n",
       "11       9055469620715506713.1628883994.1628883994  2108390820.162888   \n",
       "12       9055469620715506713.1633110583.1633110583  2108390820.162888   \n",
       "13       9055469620715506713.1635878177.1635878177  2108390820.162888   \n",
       "28       9055505230298952295.1638478433.1638478433  2108399111.163848   \n",
       "...                                            ...                ...   \n",
       "1860026  9055355469082180480.1636350848.1636350848  2108364242.163635   \n",
       "1860029  9055376699099939975.1630766214.1630766214  2108369185.163077   \n",
       "1860032  9055394269810294140.1629912447.1629912447  2108373276.162991   \n",
       "1860038  9055421130527858185.1622007305.1622007305  2108379530.162201   \n",
       "1860039  9055422955903931195.1636979515.1636979515  2108379955.163697   \n",
       "\n",
       "        visit_date visit_time  visit_number            utm_source utm_medium  \\\n",
       "5       2021-05-31   12:00:00             1  kjsLglQLzykiRbcDiGcD    organic   \n",
       "11      2021-08-13   22:00:00             1  fDLlAcSmythWSCVMvqvL     (none)   \n",
       "12      2021-10-01   20:00:00             2  fDLlAcSmythWSCVMvqvL     (none)   \n",
       "13      2021-11-02   21:36:17             3  gVRrcxiDQubJiljoTbGm   referral   \n",
       "28      2021-12-02   23:53:53             1  fDLlAcSmythWSCVMvqvL     (none)   \n",
       "...            ...        ...           ...                   ...        ...   \n",
       "1860026 2021-11-08   08:54:08             1  fDLlAcSmythWSCVMvqvL     (none)   \n",
       "1860029 2021-09-04   17:00:00             1  fDLlAcSmythWSCVMvqvL     (none)   \n",
       "1860032 2021-08-25   20:00:00             1  bByPQxmDaMXgpHeypKSM   referral   \n",
       "1860038 2021-05-26   08:00:00             1  fDLlAcSmythWSCVMvqvL     (none)   \n",
       "1860039 2021-11-15   15:31:55             1  fDLlAcSmythWSCVMvqvL     (none)   \n",
       "\n",
       "                 utm_campaign         utm_adcontent           utm_keyword  \\\n",
       "5        LTuZkdKfxRGVceoWkVyg  JNHcPlZPxEMWDnRiyoBf                   NaN   \n",
       "11       LTuZkdKfxRGVceoWkVyg  JNHcPlZPxEMWDnRiyoBf                   NaN   \n",
       "12       LTuZkdKfxRGVceoWkVyg  JNHcPlZPxEMWDnRiyoBf                   NaN   \n",
       "13       LTuZkdKfxRGVceoWkVyg  JNHcPlZPxEMWDnRiyoBf  puhZPIYqKXeFPaUviSjo   \n",
       "28       LTuZkdKfxRGVceoWkVyg  JNHcPlZPxEMWDnRiyoBf  puhZPIYqKXeFPaUviSjo   \n",
       "...                       ...                   ...                   ...   \n",
       "1860026  LTuZkdKfxRGVceoWkVyg  JNHcPlZPxEMWDnRiyoBf  puhZPIYqKXeFPaUviSjo   \n",
       "1860029  LTuZkdKfxRGVceoWkVyg  JNHcPlZPxEMWDnRiyoBf                   NaN   \n",
       "1860032  LTuZkdKfxRGVceoWkVyg  JNHcPlZPxEMWDnRiyoBf                   NaN   \n",
       "1860038  LTuZkdKfxRGVceoWkVyg  JNHcPlZPxEMWDnRiyoBf                   NaN   \n",
       "1860039  LTuZkdKfxRGVceoWkVyg  JNHcPlZPxEMWDnRiyoBf  puhZPIYqKXeFPaUviSjo   \n",
       "\n",
       "        device_category device_os device_brand device_model  \\\n",
       "5                mobile       NaN        Apple          NaN   \n",
       "11               mobile       NaN      Samsung          NaN   \n",
       "12               mobile       NaN      Samsung          NaN   \n",
       "13               mobile   Android      Samsung          NaN   \n",
       "28              desktop   Windows          NaN          NaN   \n",
       "...                 ...       ...          ...          ...   \n",
       "1860026          mobile   Android       Xiaomi          NaN   \n",
       "1860029         desktop       NaN          NaN          NaN   \n",
       "1860032          mobile       NaN      Samsung          NaN   \n",
       "1860038          mobile       NaN        Apple          NaN   \n",
       "1860039          mobile       iOS        Apple          NaN   \n",
       "\n",
       "        device_screen_resolution   device_browser geo_country  \\\n",
       "5                        375x812           Safari      Russia   \n",
       "11                       412x869  Android Webview      Russia   \n",
       "12                       412x869  Android Webview      Russia   \n",
       "13                       412x869  Android Webview      Russia   \n",
       "28                      1536x864           Chrome      Russia   \n",
       "...                          ...              ...         ...   \n",
       "1860026                  393x873           Chrome      Russia   \n",
       "1860029                1920x1080           Chrome      Russia   \n",
       "1860032                  360x800  Android Webview      Russia   \n",
       "1860038                  390x844           Safari      Russia   \n",
       "1860039                  375x667           Safari      Russia   \n",
       "\n",
       "                 geo_city  month  \n",
       "5        Saint Petersburg      5  \n",
       "11       Saint Petersburg      8  \n",
       "12       Saint Petersburg     10  \n",
       "13                  Sochi     11  \n",
       "28             Balashikha     12  \n",
       "...                   ...    ...  \n",
       "1860026            Moscow     11  \n",
       "1860029            Khimki      9  \n",
       "1860032  Saint Petersburg      8  \n",
       "1860038         Stavropol      5  \n",
       "1860039            Moscow     11  \n",
       "\n",
       "[515659 rows x 19 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_utm_medium = df_sessions[(df_sessions['utm_medium'] == '(none)') | \n",
    "                            (df_sessions['utm_medium'] == 'organic') | \n",
    "                            (df_sessions['utm_medium'] == 'referral')]\n",
    "df_utm_medium"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "66d41485",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df_sessions['traffic'] = df_utm_medium.apply(lambda x: 'organic', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a23cb155",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sessions.traffic = df_sessions.traffic.fillna('non_organic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7227a6c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "non_organic    1344383\n",
       "organic         515659\n",
       "Name: traffic, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sessions['traffic'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cd4cea8",
   "metadata": {},
   "source": [
    "### Проверка по типам устройств"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c94d960",
   "metadata": {},
   "source": [
    "Здесь мы изменим тип данных на category для device_category и проверим уникальные значения столбца для дальнейшей проверки гипотез"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ea4933c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mobile     1474871\n",
       "desktop     366863\n",
       "tablet       18308\n",
       "Name: device_category, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sessions.device_category.astype('category')\n",
    "df_sessions.device_category.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "008627a3",
   "metadata": {},
   "source": [
    "### Разделение регионов и выделение МО и СПБ"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f918ffc7",
   "metadata": {},
   "source": [
    "Далее мы проверим уникальные значения и выделим датасет для столбца geo_city"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e8864f27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Moscow              805329\n",
       "Saint Petersburg    296788\n",
       "(not set)            78172\n",
       "Yekaterinburg        35788\n",
       "Krasnodar            32243\n",
       "                     ...  \n",
       "Sherbrooke               1\n",
       "Albion                   1\n",
       "Bornheim                 1\n",
       "Huntley                  1\n",
       "Sommerda                 1\n",
       "Name: geo_city, Length: 2548, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sessions.geo_city.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e7f8402d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>session_id</th>\n",
       "      <th>client_id</th>\n",
       "      <th>visit_date</th>\n",
       "      <th>visit_time</th>\n",
       "      <th>visit_number</th>\n",
       "      <th>utm_source</th>\n",
       "      <th>utm_medium</th>\n",
       "      <th>utm_campaign</th>\n",
       "      <th>utm_adcontent</th>\n",
       "      <th>utm_keyword</th>\n",
       "      <th>device_category</th>\n",
       "      <th>device_os</th>\n",
       "      <th>device_brand</th>\n",
       "      <th>device_model</th>\n",
       "      <th>device_screen_resolution</th>\n",
       "      <th>device_browser</th>\n",
       "      <th>geo_country</th>\n",
       "      <th>geo_city</th>\n",
       "      <th>month</th>\n",
       "      <th>traffic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>905544597018549464.1636867290.1636867290</td>\n",
       "      <td>210838531.163687</td>\n",
       "      <td>2021-11-14</td>\n",
       "      <td>08:21:30</td>\n",
       "      <td>1</td>\n",
       "      <td>MvfHsxITijuriZxsqZqt</td>\n",
       "      <td>cpm</td>\n",
       "      <td>FTjNLDyTrXaWYgZymFkV</td>\n",
       "      <td>xhoenQgDQsgfEPYNPwKO</td>\n",
       "      <td>IGUCNvHlhfHpROGclCit</td>\n",
       "      <td>mobile</td>\n",
       "      <td>Android</td>\n",
       "      <td>Samsung</td>\n",
       "      <td>NaN</td>\n",
       "      <td>385x854</td>\n",
       "      <td>Samsung Internet</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Moscow</td>\n",
       "      <td>11</td>\n",
       "      <td>non_organic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9055447046360770272.1622255328.1622255328</td>\n",
       "      <td>2108385564.162225</td>\n",
       "      <td>2021-05-29</td>\n",
       "      <td>05:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>kjsLglQLzykiRbcDiGcD</td>\n",
       "      <td>cpc</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NOBKLgtuvqYWkXQHeYWM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mobile</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Xiaomi</td>\n",
       "      <td>NaN</td>\n",
       "      <td>393x786</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Moscow</td>\n",
       "      <td>5</td>\n",
       "      <td>non_organic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9055447046360770272.1622255345.1622255345</td>\n",
       "      <td>2108385564.162225</td>\n",
       "      <td>2021-05-29</td>\n",
       "      <td>05:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>kjsLglQLzykiRbcDiGcD</td>\n",
       "      <td>cpc</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mobile</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Xiaomi</td>\n",
       "      <td>NaN</td>\n",
       "      <td>393x786</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Moscow</td>\n",
       "      <td>5</td>\n",
       "      <td>non_organic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9055447192389856083.1622453074.1622453074</td>\n",
       "      <td>2108385598.162245</td>\n",
       "      <td>2021-05-31</td>\n",
       "      <td>12:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>kjsLglQLzykiRbcDiGcD</td>\n",
       "      <td>organic</td>\n",
       "      <td>LTuZkdKfxRGVceoWkVyg</td>\n",
       "      <td>JNHcPlZPxEMWDnRiyoBf</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mobile</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Apple</td>\n",
       "      <td>NaN</td>\n",
       "      <td>375x812</td>\n",
       "      <td>Safari</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>5</td>\n",
       "      <td>organic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9055455318486370642.1640843788.1640843788</td>\n",
       "      <td>2108387490.164084</td>\n",
       "      <td>2021-12-30</td>\n",
       "      <td>08:56:28</td>\n",
       "      <td>1</td>\n",
       "      <td>TxKUcPpthBDPieTGmVhx</td>\n",
       "      <td>cpc</td>\n",
       "      <td>FTjNLDyTrXaWYgZymFkV</td>\n",
       "      <td>LcGIUNPUAmXtQJaDfFBR</td>\n",
       "      <td>PwscUHjoUJDrtfWESIHj</td>\n",
       "      <td>tablet</td>\n",
       "      <td>Android</td>\n",
       "      <td>Lenovo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>602x1029</td>\n",
       "      <td>YaBrowser</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>12</td>\n",
       "      <td>non_organic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1860032</th>\n",
       "      <td>9055394269810294140.1629912447.1629912447</td>\n",
       "      <td>2108373276.162991</td>\n",
       "      <td>2021-08-25</td>\n",
       "      <td>20:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>bByPQxmDaMXgpHeypKSM</td>\n",
       "      <td>referral</td>\n",
       "      <td>LTuZkdKfxRGVceoWkVyg</td>\n",
       "      <td>JNHcPlZPxEMWDnRiyoBf</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mobile</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Samsung</td>\n",
       "      <td>NaN</td>\n",
       "      <td>360x800</td>\n",
       "      <td>Android Webview</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>8</td>\n",
       "      <td>organic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1860035</th>\n",
       "      <td>9055398929844789828.1624891784.1624891784</td>\n",
       "      <td>2108374361.162489</td>\n",
       "      <td>2021-06-28</td>\n",
       "      <td>17:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>kjsLglQLzykiRbcDiGcD</td>\n",
       "      <td>cpc</td>\n",
       "      <td>vXsFkagGabkcWKlgLzSg</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mobile</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Samsung</td>\n",
       "      <td>NaN</td>\n",
       "      <td>320x676</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Naro-Fominsk</td>\n",
       "      <td>6</td>\n",
       "      <td>non_organic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1860037</th>\n",
       "      <td>9055415581448263752.1640159305.1640159305</td>\n",
       "      <td>2108378238.164016</td>\n",
       "      <td>2021-12-22</td>\n",
       "      <td>10:48:25</td>\n",
       "      <td>1</td>\n",
       "      <td>BHcvLfOaCWvWTykYqHVe</td>\n",
       "      <td>cpc</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>VlqBmecIOXWjCWUmQkLd</td>\n",
       "      <td>desktop</td>\n",
       "      <td>Windows</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1920x1080</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Moscow</td>\n",
       "      <td>12</td>\n",
       "      <td>non_organic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1860039</th>\n",
       "      <td>9055422955903931195.1636979515.1636979515</td>\n",
       "      <td>2108379955.163697</td>\n",
       "      <td>2021-11-15</td>\n",
       "      <td>15:31:55</td>\n",
       "      <td>1</td>\n",
       "      <td>fDLlAcSmythWSCVMvqvL</td>\n",
       "      <td>(none)</td>\n",
       "      <td>LTuZkdKfxRGVceoWkVyg</td>\n",
       "      <td>JNHcPlZPxEMWDnRiyoBf</td>\n",
       "      <td>puhZPIYqKXeFPaUviSjo</td>\n",
       "      <td>mobile</td>\n",
       "      <td>iOS</td>\n",
       "      <td>Apple</td>\n",
       "      <td>NaN</td>\n",
       "      <td>375x667</td>\n",
       "      <td>Safari</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Moscow</td>\n",
       "      <td>11</td>\n",
       "      <td>organic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1860041</th>\n",
       "      <td>9055430416266113553.1640968742.1640968742</td>\n",
       "      <td>2108381692.164097</td>\n",
       "      <td>2021-12-31</td>\n",
       "      <td>19:39:02</td>\n",
       "      <td>1</td>\n",
       "      <td>fgymSoTvjKPEgaIJqsiH</td>\n",
       "      <td>cpm</td>\n",
       "      <td>FTjNLDyTrXaWYgZymFkV</td>\n",
       "      <td>yYdBRbPmBMUZHXwqGxNx</td>\n",
       "      <td>oKjXDUsycmahkgMhGdAR</td>\n",
       "      <td>desktop</td>\n",
       "      <td>Linux</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Moscow</td>\n",
       "      <td>12</td>\n",
       "      <td>non_organic</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1207444 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        session_id          client_id  \\\n",
       "1         905544597018549464.1636867290.1636867290   210838531.163687   \n",
       "3        9055447046360770272.1622255328.1622255328  2108385564.162225   \n",
       "4        9055447046360770272.1622255345.1622255345  2108385564.162225   \n",
       "5        9055447192389856083.1622453074.1622453074  2108385598.162245   \n",
       "6        9055455318486370642.1640843788.1640843788  2108387490.164084   \n",
       "...                                            ...                ...   \n",
       "1860032  9055394269810294140.1629912447.1629912447  2108373276.162991   \n",
       "1860035  9055398929844789828.1624891784.1624891784  2108374361.162489   \n",
       "1860037  9055415581448263752.1640159305.1640159305  2108378238.164016   \n",
       "1860039  9055422955903931195.1636979515.1636979515  2108379955.163697   \n",
       "1860041  9055430416266113553.1640968742.1640968742  2108381692.164097   \n",
       "\n",
       "        visit_date visit_time  visit_number            utm_source utm_medium  \\\n",
       "1       2021-11-14   08:21:30             1  MvfHsxITijuriZxsqZqt        cpm   \n",
       "3       2021-05-29   05:00:00             1  kjsLglQLzykiRbcDiGcD        cpc   \n",
       "4       2021-05-29   05:00:00             2  kjsLglQLzykiRbcDiGcD        cpc   \n",
       "5       2021-05-31   12:00:00             1  kjsLglQLzykiRbcDiGcD    organic   \n",
       "6       2021-12-30   08:56:28             1  TxKUcPpthBDPieTGmVhx        cpc   \n",
       "...            ...        ...           ...                   ...        ...   \n",
       "1860032 2021-08-25   20:00:00             1  bByPQxmDaMXgpHeypKSM   referral   \n",
       "1860035 2021-06-28   17:00:00             1  kjsLglQLzykiRbcDiGcD        cpc   \n",
       "1860037 2021-12-22   10:48:25             1  BHcvLfOaCWvWTykYqHVe        cpc   \n",
       "1860039 2021-11-15   15:31:55             1  fDLlAcSmythWSCVMvqvL     (none)   \n",
       "1860041 2021-12-31   19:39:02             1  fgymSoTvjKPEgaIJqsiH        cpm   \n",
       "\n",
       "                 utm_campaign         utm_adcontent           utm_keyword  \\\n",
       "1        FTjNLDyTrXaWYgZymFkV  xhoenQgDQsgfEPYNPwKO  IGUCNvHlhfHpROGclCit   \n",
       "3                         NaN  NOBKLgtuvqYWkXQHeYWM                   NaN   \n",
       "4                         NaN                   NaN                   NaN   \n",
       "5        LTuZkdKfxRGVceoWkVyg  JNHcPlZPxEMWDnRiyoBf                   NaN   \n",
       "6        FTjNLDyTrXaWYgZymFkV  LcGIUNPUAmXtQJaDfFBR  PwscUHjoUJDrtfWESIHj   \n",
       "...                       ...                   ...                   ...   \n",
       "1860032  LTuZkdKfxRGVceoWkVyg  JNHcPlZPxEMWDnRiyoBf                   NaN   \n",
       "1860035  vXsFkagGabkcWKlgLzSg                   NaN                   NaN   \n",
       "1860037                   NaN                   NaN  VlqBmecIOXWjCWUmQkLd   \n",
       "1860039  LTuZkdKfxRGVceoWkVyg  JNHcPlZPxEMWDnRiyoBf  puhZPIYqKXeFPaUviSjo   \n",
       "1860041  FTjNLDyTrXaWYgZymFkV  yYdBRbPmBMUZHXwqGxNx  oKjXDUsycmahkgMhGdAR   \n",
       "\n",
       "        device_category device_os device_brand device_model  \\\n",
       "1                mobile   Android      Samsung          NaN   \n",
       "3                mobile       NaN       Xiaomi          NaN   \n",
       "4                mobile       NaN       Xiaomi          NaN   \n",
       "5                mobile       NaN        Apple          NaN   \n",
       "6                tablet   Android       Lenovo          NaN   \n",
       "...                 ...       ...          ...          ...   \n",
       "1860032          mobile       NaN      Samsung          NaN   \n",
       "1860035          mobile       NaN      Samsung          NaN   \n",
       "1860037         desktop   Windows          NaN          NaN   \n",
       "1860039          mobile       iOS        Apple          NaN   \n",
       "1860041         desktop     Linux          NaN          NaN   \n",
       "\n",
       "        device_screen_resolution    device_browser geo_country  \\\n",
       "1                        385x854  Samsung Internet      Russia   \n",
       "3                        393x786            Chrome      Russia   \n",
       "4                        393x786            Chrome      Russia   \n",
       "5                        375x812            Safari      Russia   \n",
       "6                       602x1029         YaBrowser      Russia   \n",
       "...                          ...               ...         ...   \n",
       "1860032                  360x800   Android Webview      Russia   \n",
       "1860035                  320x676            Chrome      Russia   \n",
       "1860037                1920x1080            Chrome      Russia   \n",
       "1860039                  375x667            Safari      Russia   \n",
       "1860041                 1366x768            Chrome      Russia   \n",
       "\n",
       "                 geo_city  month      traffic  \n",
       "1                  Moscow     11  non_organic  \n",
       "3                  Moscow      5  non_organic  \n",
       "4                  Moscow      5  non_organic  \n",
       "5        Saint Petersburg      5      organic  \n",
       "6        Saint Petersburg     12  non_organic  \n",
       "...                   ...    ...          ...  \n",
       "1860032  Saint Petersburg      8      organic  \n",
       "1860035      Naro-Fominsk      6  non_organic  \n",
       "1860037            Moscow     12  non_organic  \n",
       "1860039            Moscow     11      organic  \n",
       "1860041            Moscow     12  non_organic  \n",
       "\n",
       "[1207444 rows x 20 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_region = df_sessions[(df_sessions['geo_city'] == 'Aprelevka') |\n",
    "                     (df_sessions['geo_city'] == 'Balashikha') |\n",
    "                     (df_sessions['geo_city'] == 'Beloozyorskiy') |\n",
    "                     (df_sessions['geo_city'] == 'Chekhov') |\n",
    "                     (df_sessions['geo_city'] == 'Chernogolovka') |\n",
    "                     (df_sessions['geo_city'] == 'Dedovsk') |\n",
    "                     (df_sessions['geo_city'] == 'Dmitrov') |\n",
    "                     (df_sessions['geo_city'] == 'Dolgoprudny') |\n",
    "                     (df_sessions['geo_city'] == 'Domodedovo') |\n",
    "                     (df_sessions['geo_city'] == 'Dubna') |\n",
    "                     (df_sessions['geo_city'] == 'Dzerzhinsky') |\n",
    "                     (df_sessions['geo_city'] == 'Elektrogorsk') |\n",
    "                     (df_sessions['geo_city'] == 'Elektrostal') | \n",
    "                     (df_sessions['geo_city'] == 'Elektrougli') |\n",
    "                     (df_sessions['geo_city'] == 'Fryazino') |\n",
    "                     (df_sessions['geo_city'] == 'Golitsyno') |\n",
    "                     (df_sessions['geo_city'] == 'Istra') |\n",
    "                     (df_sessions['geo_city'] == 'Ivanteyevka') |\n",
    "                     (df_sessions['geo_city'] == 'Izhevsk') |\n",
    "                     (df_sessions['geo_city'] == 'Kashira') |\n",
    "                     (df_sessions['geo_city'] == 'Khimki') |\n",
    "                     (df_sessions['geo_city'] == 'Khotkovo') |\n",
    "                     (df_sessions['geo_city'] == 'Klin') |\n",
    "                     (df_sessions['geo_city'] == 'Kolomna') |\n",
    "                     (df_sessions['geo_city'] == 'Korolyov') |\n",
    "                     (df_sessions['geo_city'] == 'Kotelniki') |\n",
    "                     (df_sessions['geo_city'] == 'Krasnoarmeysk') |\n",
    "                     (df_sessions['geo_city'] == 'Krasnogorsk') |\n",
    "                     (df_sessions['geo_city'] == 'Krasnoznamensk') |\n",
    "                     (df_sessions['geo_city'] == 'Kubinka') |\n",
    "                     (df_sessions['geo_city'] == 'Kurovskoye') |\n",
    "                     (df_sessions['geo_city'] == 'Likino-Dulyovo') |\n",
    "                     (df_sessions['geo_city'] == 'Lobnya') |\n",
    "                     (df_sessions['geo_city'] == 'Losino-Petrovsky') |\n",
    "                     (df_sessions['geo_city'] == 'Lukhovitsy') |\n",
    "                     (df_sessions['geo_city'] == 'Lytkarino') |\n",
    "                     (df_sessions['geo_city'] == 'Lyubertsy') |\n",
    "                     (df_sessions['geo_city'] == 'Moscow') |\n",
    "                     (df_sessions['geo_city'] == 'Mozhaysk') |\n",
    "                     (df_sessions['geo_city'] == 'Mytishchi') |\n",
    "                     (df_sessions['geo_city'] == 'Naro-Fominsk') |\n",
    "                     (df_sessions['geo_city'] == 'Noginsk') |\n",
    "                     (df_sessions['geo_city'] == 'Odintsovo') |\n",
    "                     (df_sessions['geo_city'] == 'Orekhovo-Zuyevo') |\n",
    "                     (df_sessions['geo_city'] == 'Pavlovsky Posad') |\n",
    "                     (df_sessions['geo_city'] == 'Podolsk') |\n",
    "                     (df_sessions['geo_city'] == 'Protvino') |\n",
    "                     (df_sessions['geo_city'] == 'Pushchino') |\n",
    "                     (df_sessions['geo_city'] == 'Pushkino') |\n",
    "                     (df_sessions['geo_city'] == 'Ramenskoye') |\n",
    "                     (df_sessions['geo_city'] == 'Reutov') |\n",
    "                     (df_sessions['geo_city'] == 'Ruza') |\n",
    "                     (df_sessions['geo_city'] == 'Saint Petersburg') |\n",
    "                     (df_sessions['geo_city'] == 'Sergiyev Posad') |\n",
    "                     (df_sessions['geo_city'] == 'Serpukhov') |\n",
    "                     (df_sessions['geo_city'] == 'Solnechnogorsk') |\n",
    "                     (df_sessions['geo_city'] == 'Staraya Kupavna') |\n",
    "                     (df_sessions['geo_city'] == 'Stupino') |\n",
    "                     (df_sessions['geo_city'] == 'Shchyolkovo') |\n",
    "                     (df_sessions['geo_city'] == 'Shatura') |\n",
    "                     (df_sessions['geo_city'] == 'Vidnoye') |\n",
    "                     (df_sessions['geo_city'] == 'Volokolamsk') |\n",
    "                     (df_sessions['geo_city'] == 'Voskresensk') |\n",
    "                     (df_sessions['geo_city'] == 'Yakhroma') |\n",
    "                     (df_sessions['geo_city'] == 'Zvenigorod')]\n",
    "df_region"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "129ede42",
   "metadata": {},
   "source": [
    "Далее мы создадим новый признак region и заполним значениями mo_spb те строки, которые относятся к МО и СПБ. Остальные строки в этом столбце мы заменим на значение other"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9a281277",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sessions['region'] = df_region.apply(lambda x: 'mo_spb', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f67ef58a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sessions.region = df_sessions.region.fillna('other')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "744e46e2",
   "metadata": {},
   "source": [
    "Проверим уникальные значения столбца region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "73043c89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mo_spb    1207444\n",
       "other      652598\n",
       "Name: region, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sessions.region.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0409f46",
   "metadata": {},
   "source": [
    "### Выделение моделей авто"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6e1c1ed",
   "metadata": {},
   "source": [
    "Здесь мы выделим модели и бренды авто из ссылок в столбце hit_page_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "1e35ad67",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>session_id</th>\n",
       "      <th>hit_date</th>\n",
       "      <th>hit_number</th>\n",
       "      <th>hit_type</th>\n",
       "      <th>hit_referer</th>\n",
       "      <th>hit_page_path</th>\n",
       "      <th>event_category</th>\n",
       "      <th>event_action</th>\n",
       "      <th>event_label</th>\n",
       "      <th>event_value</th>\n",
       "      <th>target_action</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>885342191847998240.1640235807.1640235807</td>\n",
       "      <td>2021-12-23 00:00:00+00:00</td>\n",
       "      <td>49</td>\n",
       "      <td>event</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sberauto.com/cars/all/volkswagen/polo/e994838f...</td>\n",
       "      <td>quiz</td>\n",
       "      <td>quiz_show</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3450086108837475701.1640265078.1640265078</td>\n",
       "      <td>2021-12-23 00:00:00+00:00</td>\n",
       "      <td>79</td>\n",
       "      <td>event</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sberauto.com/cars/all/mercedes-benz/cla-klasse...</td>\n",
       "      <td>quiz</td>\n",
       "      <td>quiz_show</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4024492994895054107.1640269084.1640269084</td>\n",
       "      <td>2021-12-23 00:00:00+00:00</td>\n",
       "      <td>85</td>\n",
       "      <td>event</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sberauto.com/cars/all/mercedes-benz/glc/f8f330...</td>\n",
       "      <td>quiz</td>\n",
       "      <td>quiz_show</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>555009234841130092.1640256620.1640256620</td>\n",
       "      <td>2021-12-23 00:00:00+00:00</td>\n",
       "      <td>101</td>\n",
       "      <td>event</td>\n",
       "      <td>VloVXNWduHeTjUoDkjkO</td>\n",
       "      <td>sberauto.com/cars/all/kia/sorento/c38179cb?utm...</td>\n",
       "      <td>quiz</td>\n",
       "      <td>quiz_show</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2692901778487480807.1640206845.1640206845</td>\n",
       "      <td>2021-12-23 00:00:00+00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>event</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sberauto.com/cars/all/nissan/x-trail/0744675f?...</td>\n",
       "      <td>card_web</td>\n",
       "      <td>view_card</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15726465</th>\n",
       "      <td>6866159858916559617.1640270865.1640270865</td>\n",
       "      <td>2021-12-23 00:00:00+00:00</td>\n",
       "      <td>43</td>\n",
       "      <td>event</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sberauto.com/cars/all/toyota/fortuner/24cb5af2...</td>\n",
       "      <td>quiz</td>\n",
       "      <td>quiz_show</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15726466</th>\n",
       "      <td>7310304587364460692.1640261783.1640261783</td>\n",
       "      <td>2021-12-23 00:00:00+00:00</td>\n",
       "      <td>40</td>\n",
       "      <td>event</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sberauto.com/cars/all/mercedes-benz/gla-klasse...</td>\n",
       "      <td>quiz</td>\n",
       "      <td>quiz_show</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15726467</th>\n",
       "      <td>8013702685784312179.1640270195.1640270195</td>\n",
       "      <td>2021-12-23 00:00:00+00:00</td>\n",
       "      <td>43</td>\n",
       "      <td>event</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sberauto.com/cars/all/toyota/alphard/2ebe4871?...</td>\n",
       "      <td>quiz</td>\n",
       "      <td>quiz_show</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15726468</th>\n",
       "      <td>8021505554734405918.1640257821.1640257821</td>\n",
       "      <td>2021-12-23 00:00:00+00:00</td>\n",
       "      <td>45</td>\n",
       "      <td>event</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sberauto.com/cars/all/bmw/x3/6a660f0a?rental_p...</td>\n",
       "      <td>quiz</td>\n",
       "      <td>quiz_show</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15726469</th>\n",
       "      <td>1569014437485249865.1640269129.1640269129</td>\n",
       "      <td>2021-12-23 00:00:00+00:00</td>\n",
       "      <td>46</td>\n",
       "      <td>event</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sberauto.com/cars/all/bmw/7-serii/399ac530?utm...</td>\n",
       "      <td>quiz</td>\n",
       "      <td>quiz_show</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3414738 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         session_id                  hit_date  \\\n",
       "2          885342191847998240.1640235807.1640235807 2021-12-23 00:00:00+00:00   \n",
       "4         3450086108837475701.1640265078.1640265078 2021-12-23 00:00:00+00:00   \n",
       "7         4024492994895054107.1640269084.1640269084 2021-12-23 00:00:00+00:00   \n",
       "8          555009234841130092.1640256620.1640256620 2021-12-23 00:00:00+00:00   \n",
       "9         2692901778487480807.1640206845.1640206845 2021-12-23 00:00:00+00:00   \n",
       "...                                             ...                       ...   \n",
       "15726465  6866159858916559617.1640270865.1640270865 2021-12-23 00:00:00+00:00   \n",
       "15726466  7310304587364460692.1640261783.1640261783 2021-12-23 00:00:00+00:00   \n",
       "15726467  8013702685784312179.1640270195.1640270195 2021-12-23 00:00:00+00:00   \n",
       "15726468  8021505554734405918.1640257821.1640257821 2021-12-23 00:00:00+00:00   \n",
       "15726469  1569014437485249865.1640269129.1640269129 2021-12-23 00:00:00+00:00   \n",
       "\n",
       "          hit_number hit_type           hit_referer  \\\n",
       "2                 49    event                   NaN   \n",
       "4                 79    event                   NaN   \n",
       "7                 85    event                   NaN   \n",
       "8                101    event  VloVXNWduHeTjUoDkjkO   \n",
       "9                  1    event                   NaN   \n",
       "...              ...      ...                   ...   \n",
       "15726465          43    event                   NaN   \n",
       "15726466          40    event                   NaN   \n",
       "15726467          43    event                   NaN   \n",
       "15726468          45    event                   NaN   \n",
       "15726469          46    event                   NaN   \n",
       "\n",
       "                                              hit_page_path event_category  \\\n",
       "2         sberauto.com/cars/all/volkswagen/polo/e994838f...           quiz   \n",
       "4         sberauto.com/cars/all/mercedes-benz/cla-klasse...           quiz   \n",
       "7         sberauto.com/cars/all/mercedes-benz/glc/f8f330...           quiz   \n",
       "8         sberauto.com/cars/all/kia/sorento/c38179cb?utm...           quiz   \n",
       "9         sberauto.com/cars/all/nissan/x-trail/0744675f?...       card_web   \n",
       "...                                                     ...            ...   \n",
       "15726465  sberauto.com/cars/all/toyota/fortuner/24cb5af2...           quiz   \n",
       "15726466  sberauto.com/cars/all/mercedes-benz/gla-klasse...           quiz   \n",
       "15726467  sberauto.com/cars/all/toyota/alphard/2ebe4871?...           quiz   \n",
       "15726468  sberauto.com/cars/all/bmw/x3/6a660f0a?rental_p...           quiz   \n",
       "15726469  sberauto.com/cars/all/bmw/7-serii/399ac530?utm...           quiz   \n",
       "\n",
       "         event_action event_label  event_value  target_action  \n",
       "2           quiz_show         NaN          NaN            0.0  \n",
       "4           quiz_show         NaN          NaN            0.0  \n",
       "7           quiz_show         NaN          NaN            0.0  \n",
       "8           quiz_show         NaN          NaN            0.0  \n",
       "9           view_card         NaN          NaN            0.0  \n",
       "...               ...         ...          ...            ...  \n",
       "15726465    quiz_show         NaN          NaN            0.0  \n",
       "15726466    quiz_show         NaN          NaN            0.0  \n",
       "15726467    quiz_show         NaN          NaN            0.0  \n",
       "15726468    quiz_show         NaN          NaN            0.0  \n",
       "15726469    quiz_show         NaN          NaN            0.0  \n",
       "\n",
       "[3414738 rows x 11 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = df_hits[(df_hits.hit_page_path.str.contains(r'\\bsberauto.com/cars/all')) |\n",
    "               df_hits.hit_page_path.str.contains(r'\\bsberauto-team.com/cars/all')]\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "fd487bd7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Процент заполненой модели в hits:  21.713315194064528\n"
     ]
    }
   ],
   "source": [
    "print('Процент заполненой модели в hits: ', len(model) / len(df_hits) *100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81399904",
   "metadata": {},
   "source": [
    "На данном этапе мы выделяем бренд из ссылки, после узнаем кол-во уникальных значений брендов и моделей"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "921199a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_hits['brand'] = model.hit_page_path.apply(lambda x: x.split('/')[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "1b88ebd6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Количество уникальных значений:  3414738\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "skoda            744486\n",
       "mercedes-benz    472316\n",
       "volkswagen       417128\n",
       "lada-vaz         403910\n",
       "nissan           238689\n",
       "kia              236270\n",
       "bmw              195391\n",
       "toyota           160840\n",
       "renault          150656\n",
       "porsche           63569\n",
       "lexus             61631\n",
       "audi              60035\n",
       "volvo             51627\n",
       "haval             51538\n",
       "mini              34709\n",
       "peugeot           27431\n",
       "land-rover        24872\n",
       "hyundai           19032\n",
       "honda               397\n",
       "infiniti            211\n",
       "Name: brand, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Количество уникальных значений: ', df_hits.brand.value_counts().sum())\n",
    "df_hits.brand.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "16ddc5ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_hits['model'] = model.hit_page_path.apply(lambda x: x.split('/')[4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "25aa7e9a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Количество уникальных значений:  3414738\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "rapid         442492\n",
       "vesta         403910\n",
       "polo          318075\n",
       "karoq         194264\n",
       "e-klasse      178467\n",
       "               ...  \n",
       "x4-m             103\n",
       "k5                36\n",
       "v-klasse          18\n",
       "cls-klasse        13\n",
       "a5                 6\n",
       "Name: model, Length: 74, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Количество уникальных значений: ', df_hits.model.value_counts().sum())\n",
    "df_hits.model.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efabc958",
   "metadata": {},
   "source": [
    "### Выделение источников из рекламных сетей"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2742037c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>session_id</th>\n",
       "      <th>client_id</th>\n",
       "      <th>visit_date</th>\n",
       "      <th>visit_time</th>\n",
       "      <th>visit_number</th>\n",
       "      <th>utm_source</th>\n",
       "      <th>utm_medium</th>\n",
       "      <th>utm_campaign</th>\n",
       "      <th>utm_adcontent</th>\n",
       "      <th>utm_keyword</th>\n",
       "      <th>...</th>\n",
       "      <th>device_os</th>\n",
       "      <th>device_brand</th>\n",
       "      <th>device_model</th>\n",
       "      <th>device_screen_resolution</th>\n",
       "      <th>device_browser</th>\n",
       "      <th>geo_country</th>\n",
       "      <th>geo_city</th>\n",
       "      <th>month</th>\n",
       "      <th>traffic</th>\n",
       "      <th>region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>905544597018549464.1636867290.1636867290</td>\n",
       "      <td>210838531.163687</td>\n",
       "      <td>2021-11-14</td>\n",
       "      <td>08:21:30</td>\n",
       "      <td>1</td>\n",
       "      <td>MvfHsxITijuriZxsqZqt</td>\n",
       "      <td>cpm</td>\n",
       "      <td>FTjNLDyTrXaWYgZymFkV</td>\n",
       "      <td>xhoenQgDQsgfEPYNPwKO</td>\n",
       "      <td>IGUCNvHlhfHpROGclCit</td>\n",
       "      <td>...</td>\n",
       "      <td>Android</td>\n",
       "      <td>Samsung</td>\n",
       "      <td>NaN</td>\n",
       "      <td>385x854</td>\n",
       "      <td>Samsung Internet</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Moscow</td>\n",
       "      <td>11</td>\n",
       "      <td>non_organic</td>\n",
       "      <td>mo_spb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>9055469620715506713.1635878177.1635878177</td>\n",
       "      <td>2108390820.162888</td>\n",
       "      <td>2021-11-02</td>\n",
       "      <td>21:36:17</td>\n",
       "      <td>3</td>\n",
       "      <td>gVRrcxiDQubJiljoTbGm</td>\n",
       "      <td>referral</td>\n",
       "      <td>LTuZkdKfxRGVceoWkVyg</td>\n",
       "      <td>JNHcPlZPxEMWDnRiyoBf</td>\n",
       "      <td>puhZPIYqKXeFPaUviSjo</td>\n",
       "      <td>...</td>\n",
       "      <td>Android</td>\n",
       "      <td>Samsung</td>\n",
       "      <td>NaN</td>\n",
       "      <td>412x869</td>\n",
       "      <td>Android Webview</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Sochi</td>\n",
       "      <td>11</td>\n",
       "      <td>organic</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>9055497958914887309.1634045644.1634045644</td>\n",
       "      <td>2108397418.163404</td>\n",
       "      <td>2021-10-12</td>\n",
       "      <td>16:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>MvfHsxITijuriZxsqZqt</td>\n",
       "      <td>cpm</td>\n",
       "      <td>FTjNLDyTrXaWYgZymFkV</td>\n",
       "      <td>xhoenQgDQsgfEPYNPwKO</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Xiaomi</td>\n",
       "      <td>NaN</td>\n",
       "      <td>393x851</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Moscow</td>\n",
       "      <td>10</td>\n",
       "      <td>non_organic</td>\n",
       "      <td>mo_spb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>9055497958914887309.1634046980.1634046980</td>\n",
       "      <td>2108397418.163404</td>\n",
       "      <td>2021-10-12</td>\n",
       "      <td>16:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>MvfHsxITijuriZxsqZqt</td>\n",
       "      <td>cpm</td>\n",
       "      <td>FTjNLDyTrXaWYgZymFkV</td>\n",
       "      <td>xhoenQgDQsgfEPYNPwKO</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Xiaomi</td>\n",
       "      <td>NaN</td>\n",
       "      <td>393x851</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Moscow</td>\n",
       "      <td>10</td>\n",
       "      <td>non_organic</td>\n",
       "      <td>mo_spb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>9055497958914887309.1634048072.1634048072</td>\n",
       "      <td>2108397418.163404</td>\n",
       "      <td>2021-10-12</td>\n",
       "      <td>17:00:00</td>\n",
       "      <td>3</td>\n",
       "      <td>MvfHsxITijuriZxsqZqt</td>\n",
       "      <td>cpm</td>\n",
       "      <td>FTjNLDyTrXaWYgZymFkV</td>\n",
       "      <td>xhoenQgDQsgfEPYNPwKO</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Xiaomi</td>\n",
       "      <td>NaN</td>\n",
       "      <td>393x851</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Moscow</td>\n",
       "      <td>10</td>\n",
       "      <td>non_organic</td>\n",
       "      <td>mo_spb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1860024</th>\n",
       "      <td>9055349030922605117.1632752193.1632752193</td>\n",
       "      <td>2108362743.163275</td>\n",
       "      <td>2021-09-27</td>\n",
       "      <td>17:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>MvfHsxITijuriZxsqZqt</td>\n",
       "      <td>cpm</td>\n",
       "      <td>FTjNLDyTrXaWYgZymFkV</td>\n",
       "      <td>PkybGvWbaqORmxjNunqZ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>BQ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>640x360</td>\n",
       "      <td>YaBrowser</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>9</td>\n",
       "      <td>non_organic</td>\n",
       "      <td>mo_spb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1860027</th>\n",
       "      <td>9055363711117247375.1629176721.1629176721</td>\n",
       "      <td>2108366161.162918</td>\n",
       "      <td>2021-08-17</td>\n",
       "      <td>08:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>PlbkrSYoHuZBWfYjYnfw</td>\n",
       "      <td>cpm</td>\n",
       "      <td>FTjNLDyTrXaWYgZymFkV</td>\n",
       "      <td>TuyPWsGQruPMpKvRxeBF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1920x1080</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>Russia</td>\n",
       "      <td>(not set)</td>\n",
       "      <td>8</td>\n",
       "      <td>non_organic</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1860028</th>\n",
       "      <td>9055373598132450813.1629664766.1629664766</td>\n",
       "      <td>2108368463.162966</td>\n",
       "      <td>2021-08-22</td>\n",
       "      <td>23:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>MvfHsxITijuriZxsqZqt</td>\n",
       "      <td>cpm</td>\n",
       "      <td>FTjNLDyTrXaWYgZymFkV</td>\n",
       "      <td>PkybGvWbaqORmxjNunqZ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Xiaomi</td>\n",
       "      <td>NaN</td>\n",
       "      <td>851x393</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>8</td>\n",
       "      <td>non_organic</td>\n",
       "      <td>mo_spb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1860030</th>\n",
       "      <td>9055382948278467242.1631877802.1631877802</td>\n",
       "      <td>2108370640.163188</td>\n",
       "      <td>2021-09-17</td>\n",
       "      <td>14:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>MvfHsxITijuriZxsqZqt</td>\n",
       "      <td>cpm</td>\n",
       "      <td>FTjNLDyTrXaWYgZymFkV</td>\n",
       "      <td>PkybGvWbaqORmxjNunqZ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Xiaomi</td>\n",
       "      <td>NaN</td>\n",
       "      <td>393x851</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>9</td>\n",
       "      <td>non_organic</td>\n",
       "      <td>mo_spb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1860034</th>\n",
       "      <td>9055397194683347295.1630237022.1630237022</td>\n",
       "      <td>2108373957.163024</td>\n",
       "      <td>2021-08-29</td>\n",
       "      <td>14:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>ISrKoXQCxqqYvAZICvjs</td>\n",
       "      <td>blogger_stories</td>\n",
       "      <td>zfwIehuEfWYdYrEZgRLo</td>\n",
       "      <td>JNHcPlZPxEMWDnRiyoBf</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Apple</td>\n",
       "      <td>NaN</td>\n",
       "      <td>414x896</td>\n",
       "      <td>Safari</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Zheleznodorozhny</td>\n",
       "      <td>8</td>\n",
       "      <td>non_organic</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>274227 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        session_id          client_id  \\\n",
       "1         905544597018549464.1636867290.1636867290   210838531.163687   \n",
       "13       9055469620715506713.1635878177.1635878177  2108390820.162888   \n",
       "21       9055497958914887309.1634045644.1634045644  2108397418.163404   \n",
       "22       9055497958914887309.1634046980.1634046980  2108397418.163404   \n",
       "23       9055497958914887309.1634048072.1634048072  2108397418.163404   \n",
       "...                                            ...                ...   \n",
       "1860024  9055349030922605117.1632752193.1632752193  2108362743.163275   \n",
       "1860027  9055363711117247375.1629176721.1629176721  2108366161.162918   \n",
       "1860028  9055373598132450813.1629664766.1629664766  2108368463.162966   \n",
       "1860030  9055382948278467242.1631877802.1631877802  2108370640.163188   \n",
       "1860034  9055397194683347295.1630237022.1630237022  2108373957.163024   \n",
       "\n",
       "        visit_date visit_time  visit_number            utm_source  \\\n",
       "1       2021-11-14   08:21:30             1  MvfHsxITijuriZxsqZqt   \n",
       "13      2021-11-02   21:36:17             3  gVRrcxiDQubJiljoTbGm   \n",
       "21      2021-10-12   16:00:00             1  MvfHsxITijuriZxsqZqt   \n",
       "22      2021-10-12   16:00:00             2  MvfHsxITijuriZxsqZqt   \n",
       "23      2021-10-12   17:00:00             3  MvfHsxITijuriZxsqZqt   \n",
       "...            ...        ...           ...                   ...   \n",
       "1860024 2021-09-27   17:00:00             1  MvfHsxITijuriZxsqZqt   \n",
       "1860027 2021-08-17   08:00:00             1  PlbkrSYoHuZBWfYjYnfw   \n",
       "1860028 2021-08-22   23:00:00             1  MvfHsxITijuriZxsqZqt   \n",
       "1860030 2021-09-17   14:00:00             1  MvfHsxITijuriZxsqZqt   \n",
       "1860034 2021-08-29   14:00:00             1  ISrKoXQCxqqYvAZICvjs   \n",
       "\n",
       "              utm_medium          utm_campaign         utm_adcontent  \\\n",
       "1                    cpm  FTjNLDyTrXaWYgZymFkV  xhoenQgDQsgfEPYNPwKO   \n",
       "13              referral  LTuZkdKfxRGVceoWkVyg  JNHcPlZPxEMWDnRiyoBf   \n",
       "21                   cpm  FTjNLDyTrXaWYgZymFkV  xhoenQgDQsgfEPYNPwKO   \n",
       "22                   cpm  FTjNLDyTrXaWYgZymFkV  xhoenQgDQsgfEPYNPwKO   \n",
       "23                   cpm  FTjNLDyTrXaWYgZymFkV  xhoenQgDQsgfEPYNPwKO   \n",
       "...                  ...                   ...                   ...   \n",
       "1860024              cpm  FTjNLDyTrXaWYgZymFkV  PkybGvWbaqORmxjNunqZ   \n",
       "1860027              cpm  FTjNLDyTrXaWYgZymFkV  TuyPWsGQruPMpKvRxeBF   \n",
       "1860028              cpm  FTjNLDyTrXaWYgZymFkV  PkybGvWbaqORmxjNunqZ   \n",
       "1860030              cpm  FTjNLDyTrXaWYgZymFkV  PkybGvWbaqORmxjNunqZ   \n",
       "1860034  blogger_stories  zfwIehuEfWYdYrEZgRLo  JNHcPlZPxEMWDnRiyoBf   \n",
       "\n",
       "                  utm_keyword  ... device_os device_brand device_model  \\\n",
       "1        IGUCNvHlhfHpROGclCit  ...   Android      Samsung          NaN   \n",
       "13       puhZPIYqKXeFPaUviSjo  ...   Android      Samsung          NaN   \n",
       "21                        NaN  ...       NaN       Xiaomi          NaN   \n",
       "22                        NaN  ...       NaN       Xiaomi          NaN   \n",
       "23                        NaN  ...       NaN       Xiaomi          NaN   \n",
       "...                       ...  ...       ...          ...          ...   \n",
       "1860024                   NaN  ...       NaN           BQ          NaN   \n",
       "1860027                   NaN  ...       NaN          NaN          NaN   \n",
       "1860028                   NaN  ...       NaN       Xiaomi          NaN   \n",
       "1860030                   NaN  ...       NaN       Xiaomi          NaN   \n",
       "1860034                   NaN  ...       NaN        Apple          NaN   \n",
       "\n",
       "        device_screen_resolution    device_browser geo_country  \\\n",
       "1                        385x854  Samsung Internet      Russia   \n",
       "13                       412x869   Android Webview      Russia   \n",
       "21                       393x851            Chrome      Russia   \n",
       "22                       393x851            Chrome      Russia   \n",
       "23                       393x851            Chrome      Russia   \n",
       "...                          ...               ...         ...   \n",
       "1860024                  640x360         YaBrowser      Russia   \n",
       "1860027                1920x1080            Chrome      Russia   \n",
       "1860028                  851x393            Chrome      Russia   \n",
       "1860030                  393x851            Chrome      Russia   \n",
       "1860034                  414x896            Safari      Russia   \n",
       "\n",
       "                 geo_city month      traffic  region  \n",
       "1                  Moscow    11  non_organic  mo_spb  \n",
       "13                  Sochi    11      organic   other  \n",
       "21                 Moscow    10  non_organic  mo_spb  \n",
       "22                 Moscow    10  non_organic  mo_spb  \n",
       "23                 Moscow    10  non_organic  mo_spb  \n",
       "...                   ...   ...          ...     ...  \n",
       "1860024  Saint Petersburg     9  non_organic  mo_spb  \n",
       "1860027         (not set)     8  non_organic   other  \n",
       "1860028  Saint Petersburg     8  non_organic  mo_spb  \n",
       "1860030  Saint Petersburg     9  non_organic  mo_spb  \n",
       "1860034  Zheleznodorozhny     8  non_organic   other  \n",
       "\n",
       "[274227 rows x 21 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sessions_socialnet_adv = df_sessions[(df_sessions['utm_source'] == 'QxAxdyPLuQMEcrdZWdWb') |\n",
    "                                     (df_sessions['utm_source'] == 'MvfHsxITijuriZxsqZqt') |\n",
    "                                     (df_sessions['utm_source'] == 'ISrKoXQCxqqYvAZICvjs') |\n",
    "                                     (df_sessions['utm_source'] == 'IZEXUFLARCUMynmHNBGo') |\n",
    "                                     (df_sessions['utm_source'] == 'PlbkrSYoHuZBWfYjYnfw') |\n",
    "                                     (df_sessions['utm_source'] == 'gVRrcxiDQubJiljoTbGm')]\n",
    "df_sessions_socialnet_adv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b003cc93",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sessions['adv'] = df_sessions_socialnet_adv.apply(lambda x: 1, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "8ee6312b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sessions.adv = df_sessions.adv.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "8d21f63e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          0\n",
       "1          1\n",
       "2          0\n",
       "3          0\n",
       "4          0\n",
       "          ..\n",
       "1860037    0\n",
       "1860038    0\n",
       "1860039    0\n",
       "1860040    0\n",
       "1860041    0\n",
       "Name: adv, Length: 1860042, dtype: int32"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sessions.adv.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "d90f52b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    1585815\n",
       "1.0     274227\n",
       "Name: adv, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sessions.adv.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2e84829",
   "metadata": {},
   "source": [
    "## Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebb5990a",
   "metadata": {},
   "source": [
    "### Удаление ненужных признаков"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "d529c0f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>session_id</th>\n",
       "      <th>hit_date</th>\n",
       "      <th>target_action</th>\n",
       "      <th>brand</th>\n",
       "      <th>model</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5639623078712724064.1640254056.1640254056</td>\n",
       "      <td>2021-12-23 00:00:00+00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7750352294969115059.1640271109.1640271109</td>\n",
       "      <td>2021-12-23 00:00:00+00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>885342191847998240.1640235807.1640235807</td>\n",
       "      <td>2021-12-23 00:00:00+00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>polo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>142526202120934167.1640211014.1640211014</td>\n",
       "      <td>2021-12-23 00:00:00+00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3450086108837475701.1640265078.1640265078</td>\n",
       "      <td>2021-12-23 00:00:00+00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>mercedes-benz</td>\n",
       "      <td>cla-klasse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15726465</th>\n",
       "      <td>6866159858916559617.1640270865.1640270865</td>\n",
       "      <td>2021-12-23 00:00:00+00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>toyota</td>\n",
       "      <td>fortuner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15726466</th>\n",
       "      <td>7310304587364460692.1640261783.1640261783</td>\n",
       "      <td>2021-12-23 00:00:00+00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>mercedes-benz</td>\n",
       "      <td>gla-klasse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15726467</th>\n",
       "      <td>8013702685784312179.1640270195.1640270195</td>\n",
       "      <td>2021-12-23 00:00:00+00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>toyota</td>\n",
       "      <td>alphard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15726468</th>\n",
       "      <td>8021505554734405918.1640257821.1640257821</td>\n",
       "      <td>2021-12-23 00:00:00+00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>bmw</td>\n",
       "      <td>x3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15726469</th>\n",
       "      <td>1569014437485249865.1640269129.1640269129</td>\n",
       "      <td>2021-12-23 00:00:00+00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>bmw</td>\n",
       "      <td>7-serii</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15726470 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         session_id                  hit_date  \\\n",
       "0         5639623078712724064.1640254056.1640254056 2021-12-23 00:00:00+00:00   \n",
       "1         7750352294969115059.1640271109.1640271109 2021-12-23 00:00:00+00:00   \n",
       "2          885342191847998240.1640235807.1640235807 2021-12-23 00:00:00+00:00   \n",
       "3          142526202120934167.1640211014.1640211014 2021-12-23 00:00:00+00:00   \n",
       "4         3450086108837475701.1640265078.1640265078 2021-12-23 00:00:00+00:00   \n",
       "...                                             ...                       ...   \n",
       "15726465  6866159858916559617.1640270865.1640270865 2021-12-23 00:00:00+00:00   \n",
       "15726466  7310304587364460692.1640261783.1640261783 2021-12-23 00:00:00+00:00   \n",
       "15726467  8013702685784312179.1640270195.1640270195 2021-12-23 00:00:00+00:00   \n",
       "15726468  8021505554734405918.1640257821.1640257821 2021-12-23 00:00:00+00:00   \n",
       "15726469  1569014437485249865.1640269129.1640269129 2021-12-23 00:00:00+00:00   \n",
       "\n",
       "          target_action          brand       model  \n",
       "0                   0.0            NaN         NaN  \n",
       "1                   0.0            NaN         NaN  \n",
       "2                   0.0     volkswagen        polo  \n",
       "3                   0.0            NaN         NaN  \n",
       "4                   0.0  mercedes-benz  cla-klasse  \n",
       "...                 ...            ...         ...  \n",
       "15726465            0.0         toyota    fortuner  \n",
       "15726466            0.0  mercedes-benz  gla-klasse  \n",
       "15726467            0.0         toyota     alphard  \n",
       "15726468            0.0            bmw          x3  \n",
       "15726469            0.0            bmw     7-serii  \n",
       "\n",
       "[15726470 rows x 5 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_for_drop_hits = ['hit_number', \n",
    "                     'hit_type',\n",
    "                     'hit_page_path',\n",
    "                     'event_category',\n",
    "                     'event_action',\n",
    "                     'event_label',\n",
    "                     'event_value',\n",
    "                     'hit_referer'\n",
    "                     ]\n",
    "hits = df_hits.drop(columns=col_for_drop_hits)\n",
    "hits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "7dce65cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>session_id</th>\n",
       "      <th>client_id</th>\n",
       "      <th>visit_date</th>\n",
       "      <th>utm_source</th>\n",
       "      <th>utm_campaign</th>\n",
       "      <th>device_category</th>\n",
       "      <th>device_brand</th>\n",
       "      <th>geo_country</th>\n",
       "      <th>geo_city</th>\n",
       "      <th>month</th>\n",
       "      <th>traffic</th>\n",
       "      <th>region</th>\n",
       "      <th>adv</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9055434745589932991.1637753792.1637753792</td>\n",
       "      <td>2108382700.163776</td>\n",
       "      <td>2021-11-24</td>\n",
       "      <td>ZpYIoDJMcFzVoPFsHGJL</td>\n",
       "      <td>LEoPHuyFvzoNfnzGgfcd</td>\n",
       "      <td>mobile</td>\n",
       "      <td>Huawei</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Zlatoust</td>\n",
       "      <td>11</td>\n",
       "      <td>non_organic</td>\n",
       "      <td>other</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>905544597018549464.1636867290.1636867290</td>\n",
       "      <td>210838531.163687</td>\n",
       "      <td>2021-11-14</td>\n",
       "      <td>MvfHsxITijuriZxsqZqt</td>\n",
       "      <td>FTjNLDyTrXaWYgZymFkV</td>\n",
       "      <td>mobile</td>\n",
       "      <td>Samsung</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Moscow</td>\n",
       "      <td>11</td>\n",
       "      <td>non_organic</td>\n",
       "      <td>mo_spb</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9055446045651783499.1640648526.1640648526</td>\n",
       "      <td>2108385331.164065</td>\n",
       "      <td>2021-12-28</td>\n",
       "      <td>ZpYIoDJMcFzVoPFsHGJL</td>\n",
       "      <td>LEoPHuyFvzoNfnzGgfcd</td>\n",
       "      <td>mobile</td>\n",
       "      <td>Huawei</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Krasnoyarsk</td>\n",
       "      <td>12</td>\n",
       "      <td>non_organic</td>\n",
       "      <td>other</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9055447046360770272.1622255328.1622255328</td>\n",
       "      <td>2108385564.162225</td>\n",
       "      <td>2021-05-29</td>\n",
       "      <td>kjsLglQLzykiRbcDiGcD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mobile</td>\n",
       "      <td>Xiaomi</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Moscow</td>\n",
       "      <td>5</td>\n",
       "      <td>non_organic</td>\n",
       "      <td>mo_spb</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9055447046360770272.1622255345.1622255345</td>\n",
       "      <td>2108385564.162225</td>\n",
       "      <td>2021-05-29</td>\n",
       "      <td>kjsLglQLzykiRbcDiGcD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mobile</td>\n",
       "      <td>Xiaomi</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Moscow</td>\n",
       "      <td>5</td>\n",
       "      <td>non_organic</td>\n",
       "      <td>mo_spb</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1860037</th>\n",
       "      <td>9055415581448263752.1640159305.1640159305</td>\n",
       "      <td>2108378238.164016</td>\n",
       "      <td>2021-12-22</td>\n",
       "      <td>BHcvLfOaCWvWTykYqHVe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>desktop</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Moscow</td>\n",
       "      <td>12</td>\n",
       "      <td>non_organic</td>\n",
       "      <td>mo_spb</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1860038</th>\n",
       "      <td>9055421130527858185.1622007305.1622007305</td>\n",
       "      <td>2108379530.162201</td>\n",
       "      <td>2021-05-26</td>\n",
       "      <td>fDLlAcSmythWSCVMvqvL</td>\n",
       "      <td>LTuZkdKfxRGVceoWkVyg</td>\n",
       "      <td>mobile</td>\n",
       "      <td>Apple</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Stavropol</td>\n",
       "      <td>5</td>\n",
       "      <td>organic</td>\n",
       "      <td>other</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1860039</th>\n",
       "      <td>9055422955903931195.1636979515.1636979515</td>\n",
       "      <td>2108379955.163697</td>\n",
       "      <td>2021-11-15</td>\n",
       "      <td>fDLlAcSmythWSCVMvqvL</td>\n",
       "      <td>LTuZkdKfxRGVceoWkVyg</td>\n",
       "      <td>mobile</td>\n",
       "      <td>Apple</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Moscow</td>\n",
       "      <td>11</td>\n",
       "      <td>organic</td>\n",
       "      <td>mo_spb</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1860040</th>\n",
       "      <td>905543020766873816.1638189404.1638189404</td>\n",
       "      <td>210838164.163819</td>\n",
       "      <td>2021-11-29</td>\n",
       "      <td>ZpYIoDJMcFzVoPFsHGJL</td>\n",
       "      <td>LEoPHuyFvzoNfnzGgfcd</td>\n",
       "      <td>mobile</td>\n",
       "      <td>Xiaomi</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Chelyabinsk</td>\n",
       "      <td>11</td>\n",
       "      <td>non_organic</td>\n",
       "      <td>other</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1860041</th>\n",
       "      <td>9055430416266113553.1640968742.1640968742</td>\n",
       "      <td>2108381692.164097</td>\n",
       "      <td>2021-12-31</td>\n",
       "      <td>fgymSoTvjKPEgaIJqsiH</td>\n",
       "      <td>FTjNLDyTrXaWYgZymFkV</td>\n",
       "      <td>desktop</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Moscow</td>\n",
       "      <td>12</td>\n",
       "      <td>non_organic</td>\n",
       "      <td>mo_spb</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1860042 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        session_id          client_id  \\\n",
       "0        9055434745589932991.1637753792.1637753792  2108382700.163776   \n",
       "1         905544597018549464.1636867290.1636867290   210838531.163687   \n",
       "2        9055446045651783499.1640648526.1640648526  2108385331.164065   \n",
       "3        9055447046360770272.1622255328.1622255328  2108385564.162225   \n",
       "4        9055447046360770272.1622255345.1622255345  2108385564.162225   \n",
       "...                                            ...                ...   \n",
       "1860037  9055415581448263752.1640159305.1640159305  2108378238.164016   \n",
       "1860038  9055421130527858185.1622007305.1622007305  2108379530.162201   \n",
       "1860039  9055422955903931195.1636979515.1636979515  2108379955.163697   \n",
       "1860040   905543020766873816.1638189404.1638189404   210838164.163819   \n",
       "1860041  9055430416266113553.1640968742.1640968742  2108381692.164097   \n",
       "\n",
       "        visit_date            utm_source          utm_campaign  \\\n",
       "0       2021-11-24  ZpYIoDJMcFzVoPFsHGJL  LEoPHuyFvzoNfnzGgfcd   \n",
       "1       2021-11-14  MvfHsxITijuriZxsqZqt  FTjNLDyTrXaWYgZymFkV   \n",
       "2       2021-12-28  ZpYIoDJMcFzVoPFsHGJL  LEoPHuyFvzoNfnzGgfcd   \n",
       "3       2021-05-29  kjsLglQLzykiRbcDiGcD                   NaN   \n",
       "4       2021-05-29  kjsLglQLzykiRbcDiGcD                   NaN   \n",
       "...            ...                   ...                   ...   \n",
       "1860037 2021-12-22  BHcvLfOaCWvWTykYqHVe                   NaN   \n",
       "1860038 2021-05-26  fDLlAcSmythWSCVMvqvL  LTuZkdKfxRGVceoWkVyg   \n",
       "1860039 2021-11-15  fDLlAcSmythWSCVMvqvL  LTuZkdKfxRGVceoWkVyg   \n",
       "1860040 2021-11-29  ZpYIoDJMcFzVoPFsHGJL  LEoPHuyFvzoNfnzGgfcd   \n",
       "1860041 2021-12-31  fgymSoTvjKPEgaIJqsiH  FTjNLDyTrXaWYgZymFkV   \n",
       "\n",
       "        device_category device_brand geo_country     geo_city  month  \\\n",
       "0                mobile       Huawei      Russia     Zlatoust     11   \n",
       "1                mobile      Samsung      Russia       Moscow     11   \n",
       "2                mobile       Huawei      Russia  Krasnoyarsk     12   \n",
       "3                mobile       Xiaomi      Russia       Moscow      5   \n",
       "4                mobile       Xiaomi      Russia       Moscow      5   \n",
       "...                 ...          ...         ...          ...    ...   \n",
       "1860037         desktop          NaN      Russia       Moscow     12   \n",
       "1860038          mobile        Apple      Russia    Stavropol      5   \n",
       "1860039          mobile        Apple      Russia       Moscow     11   \n",
       "1860040          mobile       Xiaomi      Russia  Chelyabinsk     11   \n",
       "1860041         desktop          NaN      Russia       Moscow     12   \n",
       "\n",
       "             traffic  region  adv  \n",
       "0        non_organic   other  0.0  \n",
       "1        non_organic  mo_spb  1.0  \n",
       "2        non_organic   other  0.0  \n",
       "3        non_organic  mo_spb  0.0  \n",
       "4        non_organic  mo_spb  0.0  \n",
       "...              ...     ...  ...  \n",
       "1860037  non_organic  mo_spb  0.0  \n",
       "1860038      organic   other  0.0  \n",
       "1860039      organic  mo_spb  0.0  \n",
       "1860040  non_organic   other  0.0  \n",
       "1860041  non_organic  mo_spb  0.0  \n",
       "\n",
       "[1860042 rows x 13 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_for_drop_sess = ['visit_number',\n",
    "                     'visit_time',                 \n",
    "                     'utm_medium',\n",
    "                     'utm_adcontent',\n",
    "                     'utm_keyword',\n",
    "                     'device_os',\n",
    "                     'device_model',\n",
    "                     'device_screen_resolution',\n",
    "                     'device_browser']\n",
    "sessions = df_sessions.drop(columns=col_for_drop_sess)\n",
    "sessions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8aba43b2",
   "metadata": {},
   "source": [
    "### Создание таблицы для работы с марками авто"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47b460c7",
   "metadata": {},
   "source": [
    "Здесь нам нужны только те строки, где модель и марка авто заполнены, марка заполнена в большем количестве строк, поэтому берем ее. Пропуски в столбце model заполним значением other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "be9de4ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>session_id</th>\n",
       "      <th>hit_date</th>\n",
       "      <th>target_action</th>\n",
       "      <th>brand</th>\n",
       "      <th>model</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>885342191847998240.1640235807.1640235807</td>\n",
       "      <td>2021-12-23 00:00:00+00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>polo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3450086108837475701.1640265078.1640265078</td>\n",
       "      <td>2021-12-23 00:00:00+00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>mercedes-benz</td>\n",
       "      <td>cla-klasse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4024492994895054107.1640269084.1640269084</td>\n",
       "      <td>2021-12-23 00:00:00+00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>mercedes-benz</td>\n",
       "      <td>glc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>555009234841130092.1640256620.1640256620</td>\n",
       "      <td>2021-12-23 00:00:00+00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>kia</td>\n",
       "      <td>sorento</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2692901778487480807.1640206845.1640206845</td>\n",
       "      <td>2021-12-23 00:00:00+00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>nissan</td>\n",
       "      <td>x-trail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15726465</th>\n",
       "      <td>6866159858916559617.1640270865.1640270865</td>\n",
       "      <td>2021-12-23 00:00:00+00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>toyota</td>\n",
       "      <td>fortuner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15726466</th>\n",
       "      <td>7310304587364460692.1640261783.1640261783</td>\n",
       "      <td>2021-12-23 00:00:00+00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>mercedes-benz</td>\n",
       "      <td>gla-klasse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15726467</th>\n",
       "      <td>8013702685784312179.1640270195.1640270195</td>\n",
       "      <td>2021-12-23 00:00:00+00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>toyota</td>\n",
       "      <td>alphard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15726468</th>\n",
       "      <td>8021505554734405918.1640257821.1640257821</td>\n",
       "      <td>2021-12-23 00:00:00+00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>bmw</td>\n",
       "      <td>x3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15726469</th>\n",
       "      <td>1569014437485249865.1640269129.1640269129</td>\n",
       "      <td>2021-12-23 00:00:00+00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>bmw</td>\n",
       "      <td>7-serii</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3414738 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         session_id                  hit_date  \\\n",
       "2          885342191847998240.1640235807.1640235807 2021-12-23 00:00:00+00:00   \n",
       "4         3450086108837475701.1640265078.1640265078 2021-12-23 00:00:00+00:00   \n",
       "7         4024492994895054107.1640269084.1640269084 2021-12-23 00:00:00+00:00   \n",
       "8          555009234841130092.1640256620.1640256620 2021-12-23 00:00:00+00:00   \n",
       "9         2692901778487480807.1640206845.1640206845 2021-12-23 00:00:00+00:00   \n",
       "...                                             ...                       ...   \n",
       "15726465  6866159858916559617.1640270865.1640270865 2021-12-23 00:00:00+00:00   \n",
       "15726466  7310304587364460692.1640261783.1640261783 2021-12-23 00:00:00+00:00   \n",
       "15726467  8013702685784312179.1640270195.1640270195 2021-12-23 00:00:00+00:00   \n",
       "15726468  8021505554734405918.1640257821.1640257821 2021-12-23 00:00:00+00:00   \n",
       "15726469  1569014437485249865.1640269129.1640269129 2021-12-23 00:00:00+00:00   \n",
       "\n",
       "          target_action          brand       model  \n",
       "2                   0.0     volkswagen        polo  \n",
       "4                   0.0  mercedes-benz  cla-klasse  \n",
       "7                   0.0  mercedes-benz         glc  \n",
       "8                   0.0            kia     sorento  \n",
       "9                   0.0         nissan     x-trail  \n",
       "...                 ...            ...         ...  \n",
       "15726465            0.0         toyota    fortuner  \n",
       "15726466            0.0  mercedes-benz  gla-klasse  \n",
       "15726467            0.0         toyota     alphard  \n",
       "15726468            0.0            bmw          x3  \n",
       "15726469            0.0            bmw     7-serii  \n",
       "\n",
       "[3414738 rows x 5 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hits_brand = hits[hits.brand.notna()]\n",
    "hits_brand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "5f717c0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>session_id</th>\n",
       "      <th>hit_date</th>\n",
       "      <th>target_action</th>\n",
       "      <th>brand</th>\n",
       "      <th>model</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>2</th>\n",
       "      <td>885342191847998240.1640235807.1640235807</td>\n",
       "      <td>2021-12-23 00:00:00+00:00</td>\n",
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       "      <td>volkswagen</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3450086108837475701.1640265078.1640265078</td>\n",
       "      <td>2021-12-23 00:00:00+00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>mercedes-benz</td>\n",
       "      <td>cla-klasse</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4024492994895054107.1640269084.1640269084</td>\n",
       "      <td>2021-12-23 00:00:00+00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>mercedes-benz</td>\n",
       "      <td>glc</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>555009234841130092.1640256620.1640256620</td>\n",
       "      <td>2021-12-23 00:00:00+00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>kia</td>\n",
       "      <td>sorento</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2692901778487480807.1640206845.1640206845</td>\n",
       "      <td>2021-12-23 00:00:00+00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>nissan</td>\n",
       "      <td>x-trail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>2021-12-23 00:00:00+00:00</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>2021-12-23 00:00:00+00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>mercedes-benz</td>\n",
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       "      <td>8013702685784312179.1640270195.1640270195</td>\n",
       "      <td>2021-12-23 00:00:00+00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>toyota</td>\n",
       "      <td>alphard</td>\n",
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       "    <tr>\n",
       "      <th>15726468</th>\n",
       "      <td>8021505554734405918.1640257821.1640257821</td>\n",
       "      <td>2021-12-23 00:00:00+00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>bmw</td>\n",
       "      <td>x3</td>\n",
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       "    <tr>\n",
       "      <th>15726469</th>\n",
       "      <td>1569014437485249865.1640269129.1640269129</td>\n",
       "      <td>2021-12-23 00:00:00+00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>bmw</td>\n",
       "      <td>7-serii</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3414738 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         session_id                  hit_date  \\\n",
       "2          885342191847998240.1640235807.1640235807 2021-12-23 00:00:00+00:00   \n",
       "4         3450086108837475701.1640265078.1640265078 2021-12-23 00:00:00+00:00   \n",
       "7         4024492994895054107.1640269084.1640269084 2021-12-23 00:00:00+00:00   \n",
       "8          555009234841130092.1640256620.1640256620 2021-12-23 00:00:00+00:00   \n",
       "9         2692901778487480807.1640206845.1640206845 2021-12-23 00:00:00+00:00   \n",
       "...                                             ...                       ...   \n",
       "15726465  6866159858916559617.1640270865.1640270865 2021-12-23 00:00:00+00:00   \n",
       "15726466  7310304587364460692.1640261783.1640261783 2021-12-23 00:00:00+00:00   \n",
       "15726467  8013702685784312179.1640270195.1640270195 2021-12-23 00:00:00+00:00   \n",
       "15726468  8021505554734405918.1640257821.1640257821 2021-12-23 00:00:00+00:00   \n",
       "15726469  1569014437485249865.1640269129.1640269129 2021-12-23 00:00:00+00:00   \n",
       "\n",
       "          target_action          brand       model  \n",
       "2                   0.0     volkswagen        polo  \n",
       "4                   0.0  mercedes-benz  cla-klasse  \n",
       "7                   0.0  mercedes-benz         glc  \n",
       "8                   0.0            kia     sorento  \n",
       "9                   0.0         nissan     x-trail  \n",
       "...                 ...            ...         ...  \n",
       "15726465            0.0         toyota    fortuner  \n",
       "15726466            0.0  mercedes-benz  gla-klasse  \n",
       "15726467            0.0         toyota     alphard  \n",
       "15726468            0.0            bmw          x3  \n",
       "15726469            0.0            bmw     7-serii  \n",
       "\n",
       "[3414738 rows x 5 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hits_brand.model = hits_brand.model.fillna('other')\n",
    "hits_brand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "9789eafe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2500x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.matrix(hits_brand)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0dab55eb",
   "metadata": {},
   "source": [
    "Высчитываем % пропущенных значений в hits_brand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "bf4e995c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Процент пропущенных значений:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "session_id       0.0\n",
       "hit_date         0.0\n",
       "target_action    0.0\n",
       "brand            0.0\n",
       "model            0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_values_hb = ((hits_brand.isna().sum() / len(hits_brand)) * 100).sort_values()\n",
    "print('Процент пропущенных значений:')\n",
    "missing_values_hb"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe2dbfd3",
   "metadata": {},
   "source": [
    "Датасет готов к работе, сохраним его для дальнеших расчетов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "b596f87e",
   "metadata": {},
   "outputs": [],
   "source": [
    "hits_brand.to_csv('hits_brand.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8aa02aaa",
   "metadata": {},
   "source": [
    "### Добавление признака целевого действия к основному датасету"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2feea1f7",
   "metadata": {},
   "source": [
    "Создадим промежуточную таблицу с признаками session_id (для объединения) и target_action (для дальнейших расчетов)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "7b3144cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>session_id</th>\n",
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       "      <th>999960188766601545.1626816843.1626816843</th>\n",
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       "    <tr>\n",
       "      <th>99996598443387715.1626811203.1626811203</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>999966717128502952.1638428330.1638428330</th>\n",
       "      <td>0</td>\n",
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       "      <th>999988617151873171.1623556243.1623556243</th>\n",
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       "      <td>0</td>\n",
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       "<p>1734610 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           target\n",
       "session_id                                       \n",
       "1000009318903347362.1632663668.1632663668       0\n",
       "1000010177899156286.1635013443.1635013443       0\n",
       "1000013386240115915.1635402956.1635402956       0\n",
       "1000017303238376207.1623489300.1623489300       0\n",
       "1000020580299877109.1624943350.1624943350       0\n",
       "...                                           ...\n",
       "999960188766601545.1626816843.1626816843        0\n",
       "99996598443387715.1626811203.1626811203         0\n",
       "999966717128502952.1638428330.1638428330        0\n",
       "999988617151873171.1623556243.1623556243        0\n",
       "999989480451054428.1634311006.1634311006        0\n",
       "\n",
       "[1734610 rows x 1 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_df = hits.groupby(['session_id']).agg({'target_action':'sum'})\n",
    "target_df['target'] = target_df.target_action.apply(lambda x: 1 if x > 0 else 0)\n",
    "target_df.drop(['target_action'], inplace=True, axis=1)\n",
    "target_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "2620f39c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>session_id</th>\n",
       "      <th>target</th>\n",
       "      <th>client_id</th>\n",
       "      <th>visit_date</th>\n",
       "      <th>utm_source</th>\n",
       "      <th>utm_campaign</th>\n",
       "      <th>device_category</th>\n",
       "      <th>device_brand</th>\n",
       "      <th>geo_country</th>\n",
       "      <th>geo_city</th>\n",
       "      <th>month</th>\n",
       "      <th>traffic</th>\n",
       "      <th>region</th>\n",
       "      <th>adv</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000009318903347362.1632663668.1632663668</td>\n",
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       "      <td>2021-09-26</td>\n",
       "      <td>MvfHsxITijuriZxsqZqt</td>\n",
       "      <td>FTjNLDyTrXaWYgZymFkV</td>\n",
       "      <td>mobile</td>\n",
       "      <td>Samsung</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Gelendzhik</td>\n",
       "      <td>9.0</td>\n",
       "      <td>non_organic</td>\n",
       "      <td>other</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000010177899156286.1635013443.1635013443</td>\n",
       "      <td>0</td>\n",
       "      <td>232833013.163501</td>\n",
       "      <td>2021-10-23</td>\n",
       "      <td>fDLlAcSmythWSCVMvqvL</td>\n",
       "      <td>LTuZkdKfxRGVceoWkVyg</td>\n",
       "      <td>mobile</td>\n",
       "      <td>Samsung</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Voronezh</td>\n",
       "      <td>10.0</td>\n",
       "      <td>organic</td>\n",
       "      <td>other</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000013386240115915.1635402956.1635402956</td>\n",
       "      <td>0</td>\n",
       "      <td>232833760.16354</td>\n",
       "      <td>2021-10-28</td>\n",
       "      <td>ZpYIoDJMcFzVoPFsHGJL</td>\n",
       "      <td>gecBYcKZCPMcVYdSSzKP</td>\n",
       "      <td>mobile</td>\n",
       "      <td>Samsung</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Cherkessk</td>\n",
       "      <td>10.0</td>\n",
       "      <td>non_organic</td>\n",
       "      <td>other</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000017303238376207.1623489300.1623489300</td>\n",
       "      <td>0</td>\n",
       "      <td>232834672.162349</td>\n",
       "      <td>2021-06-12</td>\n",
       "      <td>ZpYIoDJMcFzVoPFsHGJL</td>\n",
       "      <td>LEoPHuyFvzoNfnzGgfcd</td>\n",
       "      <td>mobile</td>\n",
       "      <td>Realme</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Irkutsk</td>\n",
       "      <td>6.0</td>\n",
       "      <td>non_organic</td>\n",
       "      <td>other</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000020580299877109.1624943350.1624943350</td>\n",
       "      <td>0</td>\n",
       "      <td>232835435.162494</td>\n",
       "      <td>2021-06-29</td>\n",
       "      <td>fDLlAcSmythWSCVMvqvL</td>\n",
       "      <td>LTuZkdKfxRGVceoWkVyg</td>\n",
       "      <td>mobile</td>\n",
       "      <td>Apple</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Moscow</td>\n",
       "      <td>6.0</td>\n",
       "      <td>organic</td>\n",
       "      <td>mo_spb</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <th>1734605</th>\n",
       "      <td>999960188766601545.1626816843.1626816843</td>\n",
       "      <td>0</td>\n",
       "      <td>232821374.162682</td>\n",
       "      <td>2021-07-21</td>\n",
       "      <td>MvfHsxITijuriZxsqZqt</td>\n",
       "      <td>FTjNLDyTrXaWYgZymFkV</td>\n",
       "      <td>mobile</td>\n",
       "      <td>Huawei</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Moscow</td>\n",
       "      <td>7.0</td>\n",
       "      <td>non_organic</td>\n",
       "      <td>mo_spb</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1734606</th>\n",
       "      <td>99996598443387715.1626811203.1626811203</td>\n",
       "      <td>0</td>\n",
       "      <td>23282272.162681</td>\n",
       "      <td>2021-07-20</td>\n",
       "      <td>ZpYIoDJMcFzVoPFsHGJL</td>\n",
       "      <td>LEoPHuyFvzoNfnzGgfcd</td>\n",
       "      <td>mobile</td>\n",
       "      <td>Apple</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>7.0</td>\n",
       "      <td>non_organic</td>\n",
       "      <td>mo_spb</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1734607</th>\n",
       "      <td>999966717128502952.1638428330.1638428330</td>\n",
       "      <td>0</td>\n",
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       "      <td>2021-12-02</td>\n",
       "      <td>ZpYIoDJMcFzVoPFsHGJL</td>\n",
       "      <td>LEoPHuyFvzoNfnzGgfcd</td>\n",
       "      <td>mobile</td>\n",
       "      <td>Xiaomi</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Nizhny Novgorod</td>\n",
       "      <td>12.0</td>\n",
       "      <td>non_organic</td>\n",
       "      <td>other</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1734608</th>\n",
       "      <td>999988617151873171.1623556243.1623556243</td>\n",
       "      <td>0</td>\n",
       "      <td>232827993.162356</td>\n",
       "      <td>2021-06-13</td>\n",
       "      <td>ZpYIoDJMcFzVoPFsHGJL</td>\n",
       "      <td>LEoPHuyFvzoNfnzGgfcd</td>\n",
       "      <td>mobile</td>\n",
       "      <td>Samsung</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Chelyabinsk</td>\n",
       "      <td>6.0</td>\n",
       "      <td>non_organic</td>\n",
       "      <td>other</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1734609</th>\n",
       "      <td>999989480451054428.1634311006.1634311006</td>\n",
       "      <td>0</td>\n",
       "      <td>232828194.163431</td>\n",
       "      <td>2021-10-15</td>\n",
       "      <td>MvfHsxITijuriZxsqZqt</td>\n",
       "      <td>FTjNLDyTrXaWYgZymFkV</td>\n",
       "      <td>mobile</td>\n",
       "      <td>(not set)</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Zheleznodorozhny</td>\n",
       "      <td>10.0</td>\n",
       "      <td>non_organic</td>\n",
       "      <td>other</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1734610 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        session_id  target         client_id  \\\n",
       "0        1000009318903347362.1632663668.1632663668       0  232832813.163266   \n",
       "1        1000010177899156286.1635013443.1635013443       0  232833013.163501   \n",
       "2        1000013386240115915.1635402956.1635402956       0   232833760.16354   \n",
       "3        1000017303238376207.1623489300.1623489300       0  232834672.162349   \n",
       "4        1000020580299877109.1624943350.1624943350       0  232835435.162494   \n",
       "...                                            ...     ...               ...   \n",
       "1734605   999960188766601545.1626816843.1626816843       0  232821374.162682   \n",
       "1734606    99996598443387715.1626811203.1626811203       0   23282272.162681   \n",
       "1734607   999966717128502952.1638428330.1638428330       0  232822894.163843   \n",
       "1734608   999988617151873171.1623556243.1623556243       0  232827993.162356   \n",
       "1734609   999989480451054428.1634311006.1634311006       0  232828194.163431   \n",
       "\n",
       "        visit_date            utm_source          utm_campaign  \\\n",
       "0       2021-09-26  MvfHsxITijuriZxsqZqt  FTjNLDyTrXaWYgZymFkV   \n",
       "1       2021-10-23  fDLlAcSmythWSCVMvqvL  LTuZkdKfxRGVceoWkVyg   \n",
       "2       2021-10-28  ZpYIoDJMcFzVoPFsHGJL  gecBYcKZCPMcVYdSSzKP   \n",
       "3       2021-06-12  ZpYIoDJMcFzVoPFsHGJL  LEoPHuyFvzoNfnzGgfcd   \n",
       "4       2021-06-29  fDLlAcSmythWSCVMvqvL  LTuZkdKfxRGVceoWkVyg   \n",
       "...            ...                   ...                   ...   \n",
       "1734605 2021-07-21  MvfHsxITijuriZxsqZqt  FTjNLDyTrXaWYgZymFkV   \n",
       "1734606 2021-07-20  ZpYIoDJMcFzVoPFsHGJL  LEoPHuyFvzoNfnzGgfcd   \n",
       "1734607 2021-12-02  ZpYIoDJMcFzVoPFsHGJL  LEoPHuyFvzoNfnzGgfcd   \n",
       "1734608 2021-06-13  ZpYIoDJMcFzVoPFsHGJL  LEoPHuyFvzoNfnzGgfcd   \n",
       "1734609 2021-10-15  MvfHsxITijuriZxsqZqt  FTjNLDyTrXaWYgZymFkV   \n",
       "\n",
       "        device_category device_brand geo_country          geo_city  month  \\\n",
       "0                mobile      Samsung      Russia        Gelendzhik    9.0   \n",
       "1                mobile      Samsung      Russia          Voronezh   10.0   \n",
       "2                mobile      Samsung      Russia         Cherkessk   10.0   \n",
       "3                mobile       Realme      Russia           Irkutsk    6.0   \n",
       "4                mobile        Apple      Russia            Moscow    6.0   \n",
       "...                 ...          ...         ...               ...    ...   \n",
       "1734605          mobile       Huawei      Russia            Moscow    7.0   \n",
       "1734606          mobile        Apple      Russia  Saint Petersburg    7.0   \n",
       "1734607          mobile       Xiaomi      Russia   Nizhny Novgorod   12.0   \n",
       "1734608          mobile      Samsung      Russia       Chelyabinsk    6.0   \n",
       "1734609          mobile    (not set)      Russia  Zheleznodorozhny   10.0   \n",
       "\n",
       "             traffic  region  adv  \n",
       "0        non_organic   other  1.0  \n",
       "1            organic   other  0.0  \n",
       "2        non_organic   other  0.0  \n",
       "3        non_organic   other  0.0  \n",
       "4            organic  mo_spb  0.0  \n",
       "...              ...     ...  ...  \n",
       "1734605  non_organic  mo_spb  1.0  \n",
       "1734606  non_organic  mo_spb  0.0  \n",
       "1734607  non_organic   other  0.0  \n",
       "1734608  non_organic   other  0.0  \n",
       "1734609  non_organic   other  1.0  \n",
       "\n",
       "[1734610 rows x 14 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sessions_df = pd.merge(target_df, sessions, how='left', on='session_id')\n",
    "sessions_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22d07d61",
   "metadata": {},
   "source": [
    "#### Проверка и обработка пропусков"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "822a6685",
   "metadata": {},
   "source": [
    "Проверяем пропуски в датасете sessions_df и удалим те строки, где пропущено большинство значений, и этих строк менее 1%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "2763bb49",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Процент пропущенных значений:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "session_id          0.000000\n",
       "target              0.000000\n",
       "client_id           0.135131\n",
       "visit_date          0.135131\n",
       "device_category     0.135131\n",
       "geo_country         0.135131\n",
       "geo_city            0.135131\n",
       "month               0.135131\n",
       "traffic             0.135131\n",
       "region              0.135131\n",
       "adv                 0.135131\n",
       "utm_source          0.139513\n",
       "utm_campaign       11.393397\n",
       "device_brand       20.150927\n",
       "dtype: float64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_values_sess = ((sessions_df.isna().sum() / len(sessions_df)) * 100).sort_values()\n",
    "print('Процент пропущенных значений:')\n",
    "missing_values_sess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "736e65bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "for_drop = sessions_df[sessions_df.client_id.isna() &\n",
    "                       sessions_df.visit_date.isna() &\n",
    "                       sessions_df.device_category.isna() & \n",
    "                       sessions_df.geo_country.isna() &\n",
    "                       sessions_df.geo_city.isna() &\n",
    "                       sessions_df.traffic.isna() &\n",
    "                       sessions_df.region.isna() & \n",
    "                       sessions_df.month.isna() &\n",
    "                       sessions_df.adv.isna() &\n",
    "                       sessions_df.utm_source.isna() &\n",
    "                       sessions_df.utm_campaign.isna() & \n",
    "                       sessions_df.device_brand.isna()].index.tolist()\n",
    "sessions_df.drop(for_drop, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b75ae4f4",
   "metadata": {},
   "source": [
    "Снова проверяем пропуски"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "cc86b104",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Процент пропущенных значений:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "session_id          0.000000\n",
       "target              0.000000\n",
       "client_id           0.000000\n",
       "visit_date          0.000000\n",
       "device_category     0.000000\n",
       "geo_country         0.000000\n",
       "geo_city            0.000000\n",
       "month               0.000000\n",
       "traffic             0.000000\n",
       "region              0.000000\n",
       "adv                 0.000000\n",
       "utm_source          0.004387\n",
       "utm_campaign       11.273500\n",
       "device_brand       20.042880\n",
       "dtype: float64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_values_sess2 = ((sessions_df.isna().sum() / len(sessions_df)) * 100).sort_values()\n",
    "print('Процент пропущенных значений:')\n",
    "missing_values_sess2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c4fa19c",
   "metadata": {},
   "source": [
    "Удалим небольшой % строк, где в столбце utm_source есть пропуски и остальные пропуски заполним значением other"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "1efd2b9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "sessions_df.drop(sessions_df[sessions_df.utm_source.isna()].index.tolist(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "1eeccf40",
   "metadata": {},
   "outputs": [],
   "source": [
    "sessions_df.utm_campaign = sessions_df.utm_campaign.fillna('other')\n",
    "sessions_df.device_brand = sessions_df.device_brand.fillna('other')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f349206",
   "metadata": {},
   "source": [
    "Проверяем пропуски"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "ae7a7154",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2500x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.matrix(sessions_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "5b9d0076",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Процент пропущенных значений:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "session_id         0.0\n",
       "target             0.0\n",
       "client_id          0.0\n",
       "visit_date         0.0\n",
       "utm_source         0.0\n",
       "utm_campaign       0.0\n",
       "device_category    0.0\n",
       "device_brand       0.0\n",
       "geo_country        0.0\n",
       "geo_city           0.0\n",
       "month              0.0\n",
       "traffic            0.0\n",
       "region             0.0\n",
       "adv                0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_values_sess3 = ((sessions_df.isna().sum() / len(sessions_df)) * 100).sort_values()\n",
    "print('Процент пропущенных значений:')\n",
    "missing_values_sess3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "4fd7ff6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['session_id', 'target', 'client_id', 'visit_date', 'utm_source',\n",
       "       'utm_campaign', 'device_category', 'device_brand', 'geo_country',\n",
       "       'geo_city', 'month', 'traffic', 'region', 'adv'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sessions_df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0995541",
   "metadata": {},
   "source": [
    "Наш датасет готов к работе, сохраним его для дальнеших расчетов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "c464289e",
   "metadata": {},
   "outputs": [],
   "source": [
    "sessions_df.to_csv('sessions_df.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdf6bd73",
   "metadata": {},
   "source": [
    "## Проверка гипотез и ответы на вопросы продуктовой команды"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ebd1ff8",
   "metadata": {},
   "source": [
    "### 3.1 Органический трафик не отличается от платного с точки зрения CR в целевые события"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92267ad4",
   "metadata": {},
   "source": [
    "Создадим сводную таблицу по органическому трафику и посчитаем конверсию органического  трафика в целевые действия"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "473fdba8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>visit_date</th>\n",
       "      <th>cr_org</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-05-19</td>\n",
       "      <td>7.173601</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-05-21</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-05-22</td>\n",
       "      <td>1.443001</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-05-23</td>\n",
       "      <td>4.581901</td>\n",
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       "      <th>4</th>\n",
       "      <td>2021-05-24</td>\n",
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       "      <th>221</th>\n",
       "      <td>2021-12-27</td>\n",
       "      <td>1.617710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>2021-12-28</td>\n",
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       "      <th>223</th>\n",
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       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>2021-12-31</td>\n",
       "      <td>0.852273</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>226 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    visit_date    cr_org\n",
       "0   2021-05-19  7.173601\n",
       "1   2021-05-21  0.000000\n",
       "2   2021-05-22  1.443001\n",
       "3   2021-05-23  4.581901\n",
       "4   2021-05-24  6.499459\n",
       "..         ...       ...\n",
       "221 2021-12-27  1.617710\n",
       "222 2021-12-28  1.223865\n",
       "223 2021-12-29  1.843003\n",
       "224 2021-12-30  1.626016\n",
       "225 2021-12-31  0.852273\n",
       "\n",
       "[226 rows x 2 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calc1 = sessions_df.loc[sessions_df['traffic'] == 'organic'] \\\n",
    "                .groupby(['visit_date']) \\\n",
    "                .agg({'target':'sum', 'session_id': 'nunique'}) \\\n",
    "                .reset_index()\n",
    "calc1['cr_org'] = 100 * calc1['target'] / calc1['session_id']\n",
    "traff_organic = calc1.drop(columns=['target', 'session_id'])\n",
    "traff_organic"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "475d1d9c",
   "metadata": {},
   "source": [
    "Создадим сводную таблицу по платному трафику и посчитаем конверсию платного трафика в целевые действия и объединим обе конверсии в одну таблицу для проверки гипотезы"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "ca3db14c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>2.542373</td>\n",
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       "      <td>3.030303</td>\n",
       "    </tr>\n",
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       "      <th>...</th>\n",
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       "      <th>225</th>\n",
       "      <td>2021-12-31</td>\n",
       "      <td>0.730613</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>226 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    visit_date   cr_paid\n",
       "0   2021-05-19  0.000000\n",
       "1   2021-05-21  0.000000\n",
       "2   2021-05-22  1.470588\n",
       "3   2021-05-23  2.542373\n",
       "4   2021-05-24  3.030303\n",
       "..         ...       ...\n",
       "221 2021-12-27  2.214399\n",
       "222 2021-12-28  2.191194\n",
       "223 2021-12-29  1.763571\n",
       "224 2021-12-30  1.358234\n",
       "225 2021-12-31  0.730613\n",
       "\n",
       "[226 rows x 2 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calc2 = sessions_df.loc[sessions_df['traffic'] == 'non_organic'] \\\n",
    "                .groupby(['visit_date']) \\\n",
    "                .agg({'target':'sum', 'session_id': 'nunique'}) \\\n",
    "                .reset_index()\n",
    "calc2['cr_paid'] = 100 * calc2['target'] / calc2['session_id']\n",
    "traff_paid = calc2.drop(columns=['target', 'session_id'])\n",
    "traff_paid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "00d27488",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>223</th>\n",
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       "      <td>0.730613</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>226 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    visit_date    cr_org   cr_paid\n",
       "0   2021-05-19  7.173601  0.000000\n",
       "1   2021-05-21  0.000000  0.000000\n",
       "2   2021-05-22  1.443001  1.470588\n",
       "3   2021-05-23  4.581901  2.542373\n",
       "4   2021-05-24  6.499459  3.030303\n",
       "..         ...       ...       ...\n",
       "221 2021-12-27  1.617710  2.214399\n",
       "222 2021-12-28  1.223865  2.191194\n",
       "223 2021-12-29  1.843003  1.763571\n",
       "224 2021-12-30  1.626016  1.358234\n",
       "225 2021-12-31  0.852273  0.730613\n",
       "\n",
       "[226 rows x 3 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traff_all = sessions_df.groupby(['visit_date']).agg({'session_id': 'nunique'}).reset_index()\n",
    "traff_all = traff_all.merge(traff_organic, how='left', on='visit_date')\n",
    "traff_all = traff_all.merge(traff_paid, how='left', on='visit_date')\n",
    "traff_all = traff_all.drop(columns=['session_id']).reset_index(drop=True)\n",
    "\n",
    "traff_all"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91f6fe66",
   "metadata": {},
   "source": [
    "Построим график распределения"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "3aae2201",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(16,7))\n",
    "for el in [traff_all['cr_org'], traff_all['cr_paid']]:\n",
    "    sns.distplot(el, ax=ax, kde=False)\n",
    "ax.set_xlim([0, 10])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea935eb2",
   "metadata": {},
   "source": [
    "Проверим распределение на нормальность тестом Шапиро"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "a5812073",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ShapiroResult(statistic=0.9192131161689758, pvalue=7.662628500194456e-15)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traff_sh = np.concatenate((traff_all.sort_values(by=['cr_org'])['cr_org'].values, \n",
    "                        traff_all.sort_values(by=['cr_paid'])['cr_paid'].values))\n",
    "stats.shapiro(traff_sh)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49a54533",
   "metadata": {},
   "source": [
    "Выборки имеют ненормальное распределение - используем непараметрические критерии. Выборки независимы, поэтому используем критерий Манна Уитни\n",
    "\n",
    "H0: Органический трафик не отличается от платного с точки зрения конверсии в целевые события\n",
    "\n",
    "H1: Конверсия в целевые события от органического трафика выше, чем от платного"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "8123182d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MannwhitneyuResult(statistic=39637.5, pvalue=1.5962465123037001e-24)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.mannwhitneyu(traff_all['cr_org'], traff_all['cr_paid'], alternative='greater')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28a464fb",
   "metadata": {},
   "source": [
    "Нулевая гипотеза отвергнута - принимаем альтернативную: Конверсия в целевые события от органического трафика выше, чем от платного"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d8bd35c",
   "metadata": {},
   "source": [
    "### 3.2 Трафик с мобильных устройств не отличается от трафика с декстопных устройств с точки зрения CR в целевые события"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8042800",
   "metadata": {},
   "source": [
    "Создадим сводную таблицу по мобильным устройствам и посчитаем конверсию в целевые действия"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "47cd2df5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>visit_date</th>\n",
       "      <th>cr_mob</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-05-19</td>\n",
       "      <td>6.918239</td>\n",
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       "      <th>1</th>\n",
       "      <td>2021-05-21</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-05-23</td>\n",
       "      <td>4.139715</td>\n",
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       "      <th>4</th>\n",
       "      <td>2021-05-24</td>\n",
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       "      <th>221</th>\n",
       "      <td>2021-12-27</td>\n",
       "      <td>2.032141</td>\n",
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       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>2021-12-28</td>\n",
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       "      <th>223</th>\n",
       "      <td>2021-12-29</td>\n",
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       "      <th>225</th>\n",
       "      <td>2021-12-31</td>\n",
       "      <td>1.264299</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>226 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    visit_date    cr_mob\n",
       "0   2021-05-19  6.918239\n",
       "1   2021-05-21  0.000000\n",
       "2   2021-05-22  1.369863\n",
       "3   2021-05-23  4.139715\n",
       "4   2021-05-24  6.441731\n",
       "..         ...       ...\n",
       "221 2021-12-27  2.032141\n",
       "222 2021-12-28  2.134679\n",
       "223 2021-12-29  1.890130\n",
       "224 2021-12-30  1.590281\n",
       "225 2021-12-31  1.264299\n",
       "\n",
       "[226 rows x 2 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calc3 = sessions_df.loc[(sessions_df.device_category == 'mobile') | (sessions_df.device_category == 'tablet')] \\\n",
    "                .groupby(['visit_date']) \\\n",
    "                .agg({'target':'sum', 'session_id': 'nunique'}) \\\n",
    "                .reset_index()\n",
    "calc3['cr_mob'] = 100 * calc3['target'] / calc3['session_id']\n",
    "dev_mobile = calc3.drop(columns=['target', 'session_id'])\n",
    "dev_mobile"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5240e335",
   "metadata": {},
   "source": [
    "Создадим сводную таблицу по десктопным устройствам и посчитаем конверсию в целевые действия и объединим обе конверсии в одну таблицу для проверки гипотезы"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "b95de207",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>6.499578</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>226 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    visit_date   cr_desk\n",
       "0   2021-05-19  7.272727\n",
       "1   2021-05-21  0.000000\n",
       "2   2021-05-22  1.600000\n",
       "3   2021-05-23  4.166667\n",
       "4   2021-05-24  6.499578\n",
       "..         ...       ...\n",
       "221 2021-12-27  2.450331\n",
       "222 2021-12-28  1.295160\n",
       "223 2021-12-29  0.992556\n",
       "224 2021-12-30  0.413983\n",
       "225 2021-12-31  0.032139\n",
       "\n",
       "[226 rows x 2 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calc4 = sessions_df.loc[sessions_df['device_category'] == 'desktop'] \\\n",
    "                .groupby(['visit_date']) \\\n",
    "                .agg({'target':'sum', 'session_id': 'nunique'}) \\\n",
    "                .reset_index()\n",
    "calc4['cr_desk'] = 100 * calc4['target'] / calc4['session_id']\n",
    "dev_desktop = calc4.drop(columns=['target', 'session_id'])\n",
    "dev_desktop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "631a979f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2021-05-23</td>\n",
       "      <td>4.139715</td>\n",
       "      <td>4.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-05-24</td>\n",
       "      <td>6.441731</td>\n",
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       "    </tr>\n",
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       "      <td>2.450331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>2021-12-28</td>\n",
       "      <td>2.134679</td>\n",
       "      <td>1.295160</td>\n",
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       "      <th>223</th>\n",
       "      <td>2021-12-29</td>\n",
       "      <td>1.890130</td>\n",
       "      <td>0.992556</td>\n",
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       "    <tr>\n",
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       "      <td>1.590281</td>\n",
       "      <td>0.413983</td>\n",
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       "      <td>2021-12-31</td>\n",
       "      <td>1.264299</td>\n",
       "      <td>0.032139</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>226 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    visit_date    cr_mob   cr_desk\n",
       "0   2021-05-19  6.918239  7.272727\n",
       "1   2021-05-21  0.000000  0.000000\n",
       "2   2021-05-22  1.369863  1.600000\n",
       "3   2021-05-23  4.139715  4.166667\n",
       "4   2021-05-24  6.441731  6.499578\n",
       "..         ...       ...       ...\n",
       "221 2021-12-27  2.032141  2.450331\n",
       "222 2021-12-28  2.134679  1.295160\n",
       "223 2021-12-29  1.890130  0.992556\n",
       "224 2021-12-30  1.590281  0.413983\n",
       "225 2021-12-31  1.264299  0.032139\n",
       "\n",
       "[226 rows x 3 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dev_all = sessions_df.groupby(['visit_date']).agg({'session_id': 'nunique'}).reset_index()\n",
    "dev_all = dev_all.merge(dev_mobile, how='left', on='visit_date')\n",
    "dev_all = dev_all.merge(dev_desktop, how='left', on='visit_date')\n",
    "dev_all = dev_all.drop(columns=['session_id']).reset_index(drop=True)\n",
    "\n",
    "dev_all"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1b800dc",
   "metadata": {},
   "source": [
    "Построим график распределения"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "0cbeaec2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(16,7))\n",
    "for el in [dev_all['cr_mob'], dev_all['cr_desk']]:\n",
    "    sns.distplot(el, ax=ax, kde=False)\n",
    "ax.set_xlim([0, 10])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62d6fbb5",
   "metadata": {},
   "source": [
    "Проверим распределение на нормальность тестом Шапиро"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "5a58dff1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ShapiroResult(statistic=0.870449960231781, pvalue=5.993502564375473e-19)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dev_sh = np.concatenate((dev_all.sort_values(by=['cr_mob'])['cr_mob'].values, \n",
    "                        dev_all.sort_values(by=['cr_desk'])['cr_desk'].values))\n",
    "stats.shapiro(dev_sh)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6d9b46b",
   "metadata": {},
   "source": [
    "Выборки имеют ненормальное распределение - используем непараметрические критерии. Выборки независимы, поэтому используем критерий Манна Уитни\n",
    "\n",
    "H0: Трафик с мобильных устройств не отличается от десктопного с точки зрения конверсии в целевые события\n",
    "\n",
    "H1: Конверсия в целевые события от мобильного трафика отличается от платного"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "239ca83c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MannwhitneyuResult(statistic=24634.0, pvalue=0.5152590369206732)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.mannwhitneyu(dev_all['cr_mob'], dev_all['cr_desk'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72712565",
   "metadata": {},
   "source": [
    "Нулевая гипотеза не может быть отвергнута - трафик с мобильных устройств не отличается от дестопного с точки зрения конверсии в целевые события"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0871a839",
   "metadata": {},
   "source": [
    "### 3.3 Трафик из городов присутствия (Москва и область, Санкт-Петербург) не отличается от трафика из иных регионов с точки зрения Conversion Rate в целевые события"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99e6e0cc",
   "metadata": {},
   "source": [
    "Создадим сводную таблицу по органическому трафику и посчитаем конверсию органического  трафика в целевые действия"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "8613085e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>visit_date</th>\n",
       "      <th>cr_mo_spb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-05-19</td>\n",
       "      <td>11.764706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-05-21</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-05-22</td>\n",
       "      <td>1.717557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-05-23</td>\n",
       "      <td>5.314685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-05-24</td>\n",
       "      <td>6.456763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>2021-12-27</td>\n",
       "      <td>1.995835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>2021-12-28</td>\n",
       "      <td>1.979101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>2021-12-29</td>\n",
       "      <td>1.757619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>2021-12-30</td>\n",
       "      <td>1.165283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>2021-12-31</td>\n",
       "      <td>0.566305</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>226 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    visit_date  cr_mo_spb\n",
       "0   2021-05-19  11.764706\n",
       "1   2021-05-21   0.000000\n",
       "2   2021-05-22   1.717557\n",
       "3   2021-05-23   5.314685\n",
       "4   2021-05-24   6.456763\n",
       "..         ...        ...\n",
       "221 2021-12-27   1.995835\n",
       "222 2021-12-28   1.979101\n",
       "223 2021-12-29   1.757619\n",
       "224 2021-12-30   1.165283\n",
       "225 2021-12-31   0.566305\n",
       "\n",
       "[226 rows x 2 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calc5 = sessions_df.loc[sessions_df['region'] == 'mo_spb'] \\\n",
    "                .groupby(['visit_date']) \\\n",
    "                .agg({'target':'sum', 'session_id': 'nunique'}) \\\n",
    "                .reset_index()\n",
    "calc5['cr_mo_spb'] = 100 * calc5['target'] / calc5['session_id']\n",
    "reg_mo_spb = calc5.drop(columns=['target', 'session_id'])\n",
    "reg_mo_spb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "6cf4ce74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>visit_date</th>\n",
       "      <th>target</th>\n",
       "      <th>session_id</th>\n",
       "      <th>cr_mo_spb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-05-19</td>\n",
       "      <td>50</td>\n",
       "      <td>425</td>\n",
       "      <td>11.764706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-05-21</td>\n",
       "      <td>0</td>\n",
       "      <td>543</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-05-22</td>\n",
       "      <td>9</td>\n",
       "      <td>524</td>\n",
       "      <td>1.717557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-05-23</td>\n",
       "      <td>38</td>\n",
       "      <td>715</td>\n",
       "      <td>5.314685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-05-24</td>\n",
       "      <td>1739</td>\n",
       "      <td>26933</td>\n",
       "      <td>6.456763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>2021-12-27</td>\n",
       "      <td>115</td>\n",
       "      <td>5762</td>\n",
       "      <td>1.995835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>2021-12-28</td>\n",
       "      <td>125</td>\n",
       "      <td>6316</td>\n",
       "      <td>1.979101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>2021-12-29</td>\n",
       "      <td>124</td>\n",
       "      <td>7055</td>\n",
       "      <td>1.757619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>2021-12-30</td>\n",
       "      <td>87</td>\n",
       "      <td>7466</td>\n",
       "      <td>1.165283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>2021-12-31</td>\n",
       "      <td>60</td>\n",
       "      <td>10595</td>\n",
       "      <td>0.566305</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>226 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    visit_date  target  session_id  cr_mo_spb\n",
       "0   2021-05-19      50         425  11.764706\n",
       "1   2021-05-21       0         543   0.000000\n",
       "2   2021-05-22       9         524   1.717557\n",
       "3   2021-05-23      38         715   5.314685\n",
       "4   2021-05-24    1739       26933   6.456763\n",
       "..         ...     ...         ...        ...\n",
       "221 2021-12-27     115        5762   1.995835\n",
       "222 2021-12-28     125        6316   1.979101\n",
       "223 2021-12-29     124        7055   1.757619\n",
       "224 2021-12-30      87        7466   1.165283\n",
       "225 2021-12-31      60       10595   0.566305\n",
       "\n",
       "[226 rows x 4 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calc5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65ba11bf",
   "metadata": {},
   "source": [
    "Создадим сводную таблицу по платному трафику и посчитаем конверсию платного трафика в целевые действия и объединим обе конверсии в одну таблицу для проверки гипотезы"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "94ef466f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>visit_date</th>\n",
       "      <th>cr_other</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>2021-05-19</td>\n",
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       "      <th>1</th>\n",
       "      <td>2021-05-21</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-05-22</td>\n",
       "      <td>0.843882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-05-23</td>\n",
       "      <td>2.030457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-05-24</td>\n",
       "      <td>6.481256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <th>221</th>\n",
       "      <td>2021-12-27</td>\n",
       "      <td>2.188022</td>\n",
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       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>2021-12-28</td>\n",
       "      <td>2.089060</td>\n",
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       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>2021-12-29</td>\n",
       "      <td>1.793468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>2021-12-30</td>\n",
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       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>2021-12-31</td>\n",
       "      <td>1.195017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>226 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    visit_date  cr_other\n",
       "0   2021-05-19  0.000000\n",
       "1   2021-05-21  0.000000\n",
       "2   2021-05-22  0.843882\n",
       "3   2021-05-23  2.030457\n",
       "4   2021-05-24  6.481256\n",
       "..         ...       ...\n",
       "221 2021-12-27  2.188022\n",
       "222 2021-12-28  2.089060\n",
       "223 2021-12-29  1.793468\n",
       "224 2021-12-30  1.717715\n",
       "225 2021-12-31  1.195017\n",
       "\n",
       "[226 rows x 2 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calc6 = sessions_df.loc[sessions_df['region'] == 'other'] \\\n",
    "                .groupby(['visit_date']) \\\n",
    "                .agg({'target':'sum', 'session_id': 'nunique'}) \\\n",
    "                .reset_index()\n",
    "calc6['cr_other'] = 100 * calc6['target'] / calc6['session_id']\n",
    "reg_other = calc6.drop(columns=['target', 'session_id'])\n",
    "reg_other"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "db8bc91c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>visit_date</th>\n",
       "      <th>cr_mo_spb</th>\n",
       "      <th>cr_other</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>11.764706</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>2021-05-21</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>1.717557</td>\n",
       "      <td>0.843882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-05-23</td>\n",
       "      <td>5.314685</td>\n",
       "      <td>2.030457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-05-24</td>\n",
       "      <td>6.456763</td>\n",
       "      <td>6.481256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>2021-12-27</td>\n",
       "      <td>1.995835</td>\n",
       "      <td>2.188022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>2021-12-28</td>\n",
       "      <td>1.979101</td>\n",
       "      <td>2.089060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>2021-12-29</td>\n",
       "      <td>1.757619</td>\n",
       "      <td>1.793468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>2021-12-30</td>\n",
       "      <td>1.165283</td>\n",
       "      <td>1.717715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>2021-12-31</td>\n",
       "      <td>0.566305</td>\n",
       "      <td>1.195017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>226 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    visit_date  cr_mo_spb  cr_other\n",
       "0   2021-05-19  11.764706  0.000000\n",
       "1   2021-05-21   0.000000  0.000000\n",
       "2   2021-05-22   1.717557  0.843882\n",
       "3   2021-05-23   5.314685  2.030457\n",
       "4   2021-05-24   6.456763  6.481256\n",
       "..         ...        ...       ...\n",
       "221 2021-12-27   1.995835  2.188022\n",
       "222 2021-12-28   1.979101  2.089060\n",
       "223 2021-12-29   1.757619  1.793468\n",
       "224 2021-12-30   1.165283  1.717715\n",
       "225 2021-12-31   0.566305  1.195017\n",
       "\n",
       "[226 rows x 3 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_all = sessions_df.groupby(['visit_date']).agg({'session_id': 'nunique'}).reset_index()\n",
    "reg_all = reg_all.merge(reg_mo_spb, how='left', on='visit_date')\n",
    "reg_all = reg_all.merge(reg_other, how='left', on='visit_date')\n",
    "reg_all = reg_all.drop(columns=['session_id']).reset_index(drop=True)\n",
    "\n",
    "reg_all"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "865af183",
   "metadata": {},
   "source": [
    "Построим график распределения"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "a6582894",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(16,7))\n",
    "for el in [reg_all['cr_mo_spb'], reg_all['cr_other']]:\n",
    "    sns.distplot(el, ax=ax, kde=False)\n",
    "ax.set_xlim([0, 10])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbfe98f5",
   "metadata": {},
   "source": [
    "Проверим распределение на нормальность тестом Шапиро"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "c861bcf3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ShapiroResult(statistic=0.8850669860839844, pvalue=7.422070779214766e-18)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_sh = np.concatenate((reg_all.sort_values(by=['cr_mo_spb'])['cr_mo_spb'].values, \n",
    "                        reg_all.sort_values(by=['cr_other'])['cr_other'].values))\n",
    "stats.shapiro(reg_sh)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e06df6e5",
   "metadata": {},
   "source": [
    "Выборки имеют ненормальное распределение - используем непараметрические критерии. Выборки независимы, поэтому используем критерий Манна Уитни\n",
    "\n",
    "H0: Трафик из городов присутствия не отличается от других регионов с точки зрения конверсии в целевые события\n",
    "\n",
    "H1: Конверсия в целевые события от городов присутствия отличается от других регионов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "2495cc7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MannwhitneyuResult(statistic=27303.5, pvalue=0.10184748890799017)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.mannwhitneyu(reg_all['cr_mo_spb'], reg_all['cr_other'], alternative='greater')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc252f9d",
   "metadata": {},
   "source": [
    "Нулевая гипотеза не может быть отвергнута - трафик из городов присутствия не отличается от других регионов с точки зрения конверсии в целевые события"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "739f6319",
   "metadata": {},
   "source": [
    "### 3.4 Из каких источников идет самый целевой трафик?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1360908f",
   "metadata": {},
   "source": [
    "С точки зрения CR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "d0cacdc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>utm_source</th>\n",
       "      <th colspan=\"2\" halign=\"left\">target</th>\n",
       "      <th>CR</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>count</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>YpBKcihLLfFjWuxOLfvW</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>fJCYsujgSxIHFbOmgDdN</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>87.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>XzfzEBYZWgSDtJNXOadn</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>50.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>CqeIpFwJscTsZoYXdHsP</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>50.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>yxJKymlSGVuKIPTxbysx</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>33.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               utm_source target            CR\n",
       "                             sum count        \n",
       "144  YpBKcihLLfFjWuxOLfvW      1     1  100.00\n",
       "177  fJCYsujgSxIHFbOmgDdN      7     8   87.50\n",
       "134  XzfzEBYZWgSDtJNXOadn      1     2   50.00\n",
       "16   CqeIpFwJscTsZoYXdHsP      1     2   50.00\n",
       "274  yxJKymlSGVuKIPTxbysx      1     3   33.33"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calc7 = sessions_df.groupby(['utm_source']).agg({'target':['sum', 'count']}).reset_index()\n",
    "calc7['CR'] = round((calc7['target',   'sum'] / calc7['target', 'count'] * 100),2)\n",
    "source_cr = calc7.sort_values(by=['CR'],ascending=False)\n",
    "source_cr.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57d8ac05",
   "metadata": {},
   "source": [
    "С точки зрения объема целевого трафика"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "c67f28ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>utm_source</th>\n",
       "      <th colspan=\"2\" halign=\"left\">target</th>\n",
       "      <th>CR</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>count</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>ZpYIoDJMcFzVoPFsHGJL</td>\n",
       "      <td>15998</td>\n",
       "      <td>552555</td>\n",
       "      <td>2.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>fDLlAcSmythWSCVMvqvL</td>\n",
       "      <td>10531</td>\n",
       "      <td>277060</td>\n",
       "      <td>3.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>kjsLglQLzykiRbcDiGcD</td>\n",
       "      <td>6293</td>\n",
       "      <td>245178</td>\n",
       "      <td>2.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>bByPQxmDaMXgpHeypKSM</td>\n",
       "      <td>5557</td>\n",
       "      <td>90356</td>\n",
       "      <td>6.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>BHcvLfOaCWvWTykYqHVe</td>\n",
       "      <td>3882</td>\n",
       "      <td>110963</td>\n",
       "      <td>3.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               utm_source target            CR\n",
       "                             sum   count      \n",
       "149  ZpYIoDJMcFzVoPFsHGJL  15998  552555  2.90\n",
       "176  fDLlAcSmythWSCVMvqvL  10531  277060  3.80\n",
       "210  kjsLglQLzykiRbcDiGcD   6293  245178  2.57\n",
       "156  bByPQxmDaMXgpHeypKSM   5557   90356  6.15\n",
       "5    BHcvLfOaCWvWTykYqHVe   3882  110963  3.50"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "source_vol = calc7.sort_values(by=[('target', 'sum')],ascending=False)\n",
    "source_vol.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92b9e01e",
   "metadata": {},
   "source": [
    "### 3.4 Из каких кампаний идет самый целевой трафик?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c55f009",
   "metadata": {},
   "source": [
    "С точки зрения CR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "9eb85a3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>utm_campaign</th>\n",
       "      <th colspan=\"2\" halign=\"left\">target</th>\n",
       "      <th>CR</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>count</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>MHdHrBKQwbDaRalwnlJq</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>JkhCpeDGCtTwhwqWLywv</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>IRKNegNgOUQLwudzMElF</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>87.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>SbYAsCvXapXBOIxEKBZs</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>50.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>lndNIerCYECRQvBTyTye</td>\n",
       "      <td>23</td>\n",
       "      <td>75</td>\n",
       "      <td>30.67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             utm_campaign target            CR\n",
       "                             sum count        \n",
       "95   MHdHrBKQwbDaRalwnlJq      1     1  100.00\n",
       "69   JkhCpeDGCtTwhwqWLywv      1     1  100.00\n",
       "58   IRKNegNgOUQLwudzMElF      7     8   87.50\n",
       "145  SbYAsCvXapXBOIxEKBZs      1     2   50.00\n",
       "293  lndNIerCYECRQvBTyTye     23    75   30.67"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calc8 = sessions_df.groupby(['utm_campaign']).agg({'target':['sum', 'count']}).reset_index()\n",
    "calc8['CR'] = round((calc8['target',   'sum'] / calc8['target', 'count'] * 100),2)\n",
    "campaign_cr = calc8.sort_values(by=['CR'],ascending=False)\n",
    "campaign_cr.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "181cfd24",
   "metadata": {},
   "source": [
    "С точки зрения объема целевого трафика"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "724031fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>utm_campaign</th>\n",
       "      <th colspan=\"2\" halign=\"left\">target</th>\n",
       "      <th>CR</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>count</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>LTuZkdKfxRGVceoWkVyg</td>\n",
       "      <td>19004</td>\n",
       "      <td>422965</td>\n",
       "      <td>4.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>LEoPHuyFvzoNfnzGgfcd</td>\n",
       "      <td>9348</td>\n",
       "      <td>321286</td>\n",
       "      <td>2.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>gecBYcKZCPMcVYdSSzKP</td>\n",
       "      <td>4545</td>\n",
       "      <td>133247</td>\n",
       "      <td>3.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>FTjNLDyTrXaWYgZymFkV</td>\n",
       "      <td>2447</td>\n",
       "      <td>234950</td>\n",
       "      <td>1.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>sbJRYgVfvcnqKJNDDYIr</td>\n",
       "      <td>575</td>\n",
       "      <td>19942</td>\n",
       "      <td>2.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             utm_campaign target            CR\n",
       "                             sum   count      \n",
       "87   LTuZkdKfxRGVceoWkVyg  19004  422965  4.49\n",
       "84   LEoPHuyFvzoNfnzGgfcd   9348  321286  2.91\n",
       "255  gecBYcKZCPMcVYdSSzKP   4545  133247  3.41\n",
       "39   FTjNLDyTrXaWYgZymFkV   2447  234950  1.04\n",
       "348  sbJRYgVfvcnqKJNDDYIr    575   19942  2.88"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "campaign_vol = calc8.sort_values(by=[('target', 'sum')],ascending=False)\n",
    "campaign_vol[campaign_vol.utm_campaign != 'other'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b66ea7c7",
   "metadata": {},
   "source": [
    "### 3.4 Из каких устройств идет самый целевой трафик?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02d98a32",
   "metadata": {},
   "source": [
    "С точки зрения CR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "96670c94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>device_brand</th>\n",
       "      <th colspan=\"2\" halign=\"left\">target</th>\n",
       "      <th>CR</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>count</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>Motive</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Condor</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>57.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>Land Rover</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>33.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>Vertu</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>33.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>Razer</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>16.67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    device_brand target            CR\n",
       "                    sum count        \n",
       "118       Motive      1     1  100.00\n",
       "31        Condor      4     7   57.14\n",
       "98    Land Rover      1     3   33.33\n",
       "176        Vertu      1     3   33.33\n",
       "143        Razer      1     6   16.67"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calc9 = sessions_df.groupby(['device_brand']).agg({'target':['sum', 'count']}).reset_index()\n",
    "calc9['CR'] = round((calc9['target',   'sum'] / calc9['target', 'count'] * 100),2)\n",
    "device_cr = calc9.sort_values(by=['CR'],ascending=False)\n",
    "device_cr.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11bd0975",
   "metadata": {},
   "source": [
    "C точки зрения объема целевого трафика"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "71dde705",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>device_brand</th>\n",
       "      <th colspan=\"2\" halign=\"left\">target</th>\n",
       "      <th>CR</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>count</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Apple</td>\n",
       "      <td>14467</td>\n",
       "      <td>503526</td>\n",
       "      <td>2.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>Samsung</td>\n",
       "      <td>10053</td>\n",
       "      <td>311636</td>\n",
       "      <td>3.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>Xiaomi</td>\n",
       "      <td>6592</td>\n",
       "      <td>269242</td>\n",
       "      <td>2.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Huawei</td>\n",
       "      <td>4518</td>\n",
       "      <td>173823</td>\n",
       "      <td>2.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>Realme</td>\n",
       "      <td>421</td>\n",
       "      <td>17925</td>\n",
       "      <td>2.35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    device_brand target            CR\n",
       "                    sum   count      \n",
       "10         Apple  14467  503526  2.87\n",
       "145      Samsung  10053  311636  3.23\n",
       "191       Xiaomi   6592  269242  2.45\n",
       "76        Huawei   4518  173823  2.60\n",
       "144       Realme    421   17925  2.35"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device_vol = calc9.sort_values(by=[('target', 'sum')],ascending=False)\n",
    "device_vol[(device_vol.device_brand != '(not set)') & (device_vol.device_brand != 'other')].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c44e873e",
   "metadata": {},
   "source": [
    "### 3.4 Из каких локаций идет самый целевой трафик?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14b27216",
   "metadata": {},
   "source": [
    "С точки зрения CR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "462a1144",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>geo_city</th>\n",
       "      <th colspan=\"2\" halign=\"left\">target</th>\n",
       "      <th>CR</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>count</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>Brescia</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1708</th>\n",
       "      <td>Qingdao</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>Middletown</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1498</th>\n",
       "      <td>Nybro</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>725</th>\n",
       "      <td>Gravesend</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        geo_city target           CR\n",
       "                    sum count       \n",
       "341      Brescia      1     1  100.0\n",
       "1708     Qingdao      1     1  100.0\n",
       "1305  Middletown      1     1  100.0\n",
       "1498       Nybro      1     1  100.0\n",
       "725    Gravesend      1     1  100.0"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calc10 = sessions_df.groupby(['geo_city']).agg({'target':['sum', 'count']}).reset_index()\n",
    "calc10['CR'] = round((calc10['target',   'sum'] / calc10['target', 'count'] * 100),2)\n",
    "city_cr = calc10.sort_values(by=['CR'],ascending=False)\n",
    "city_cr.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b3421af",
   "metadata": {},
   "source": [
    "С точки зрения объема целевого трафика"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "98127afc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>geo_city</th>\n",
       "      <th colspan=\"2\" halign=\"left\">target</th>\n",
       "      <th>CR</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>count</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1346</th>\n",
       "      <td>Moscow</td>\n",
       "      <td>23625</td>\n",
       "      <td>750873</td>\n",
       "      <td>3.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1811</th>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>7113</td>\n",
       "      <td>278399</td>\n",
       "      <td>2.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>937</th>\n",
       "      <td>Kazan</td>\n",
       "      <td>1139</td>\n",
       "      <td>27689</td>\n",
       "      <td>4.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1040</th>\n",
       "      <td>Krasnodar</td>\n",
       "      <td>1081</td>\n",
       "      <td>30260</td>\n",
       "      <td>3.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2329</th>\n",
       "      <td>Yekaterinburg</td>\n",
       "      <td>887</td>\n",
       "      <td>33554</td>\n",
       "      <td>2.64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              geo_city target            CR\n",
       "                          sum   count      \n",
       "1346            Moscow  23625  750873  3.15\n",
       "1811  Saint Petersburg   7113  278399  2.55\n",
       "937              Kazan   1139   27689  4.11\n",
       "1040         Krasnodar   1081   30260  3.57\n",
       "2329     Yekaterinburg    887   33554  2.64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_vol = calc10.sort_values(by=[('target', 'sum')],ascending=False)\n",
    "city_vol[(city_vol.geo_city != 'other') & (city_vol.geo_city != '(not set)')].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5eab35b5",
   "metadata": {},
   "source": [
    "### 3.5 Какие авто пользуются наибольшим спросом? У каких авто самый лучший показатель CR?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7581f80",
   "metadata": {},
   "source": [
    "Популярность"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "ec17dbf7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>brand</th>\n",
       "      <th colspan=\"2\" halign=\"left\">target_action</th>\n",
       "      <th>CR</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>count</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>skoda</td>\n",
       "      <td>8256.0</td>\n",
       "      <td>744486</td>\n",
       "      <td>1.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>mercedes-benz</td>\n",
       "      <td>2440.0</td>\n",
       "      <td>472316</td>\n",
       "      <td>0.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>volkswagen</td>\n",
       "      <td>5056.0</td>\n",
       "      <td>417128</td>\n",
       "      <td>1.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>lada-vaz</td>\n",
       "      <td>5356.0</td>\n",
       "      <td>403910</td>\n",
       "      <td>1.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>nissan</td>\n",
       "      <td>1386.0</td>\n",
       "      <td>238689</td>\n",
       "      <td>0.58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            brand target_action            CR\n",
       "                            sum   count      \n",
       "16          skoda        8256.0  744486  1.11\n",
       "10  mercedes-benz        2440.0  472316  0.52\n",
       "18     volkswagen        5056.0  417128  1.21\n",
       "7        lada-vaz        5356.0  403910  1.33\n",
       "12         nissan        1386.0  238689  0.58"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calc21 = hits_brand.groupby(['brand'], as_index=False).agg({'target_action':['sum', 'count']})\n",
    "calc21['CR'] = round((calc21['target_action',   'sum'] / calc21['target_action', 'count'] * 100),2)\n",
    "auto_vol = calc21.sort_values(by=[('target_action',   'count')],ascending=False)\n",
    "auto_vol.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d34f382",
   "metadata": {},
   "source": [
    "CR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "7b0bc3ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>brand</th>\n",
       "      <th colspan=\"2\" halign=\"left\">target_action</th>\n",
       "      <th>CR</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>count</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>infiniti</td>\n",
       "      <td>18.0</td>\n",
       "      <td>211</td>\n",
       "      <td>8.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>hyundai</td>\n",
       "      <td>445.0</td>\n",
       "      <td>19032</td>\n",
       "      <td>2.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>honda</td>\n",
       "      <td>7.0</td>\n",
       "      <td>397</td>\n",
       "      <td>1.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>lada-vaz</td>\n",
       "      <td>5356.0</td>\n",
       "      <td>403910</td>\n",
       "      <td>1.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>volkswagen</td>\n",
       "      <td>5056.0</td>\n",
       "      <td>417128</td>\n",
       "      <td>1.21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         brand target_action            CR\n",
       "                         sum   count      \n",
       "5     infiniti          18.0     211  8.53\n",
       "4      hyundai         445.0   19032  2.34\n",
       "3        honda           7.0     397  1.76\n",
       "7     lada-vaz        5356.0  403910  1.33\n",
       "18  volkswagen        5056.0  417128  1.21"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auto_cr = calc21.sort_values(by=['CR'],ascending=False)\n",
    "auto_cr.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d8fdcc3",
   "metadata": {},
   "source": [
    "### 3.6 Стоит ли увеличивать свое присутствие в соцсетях и давать там больше рекламы?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c188fe7c",
   "metadata": {},
   "source": [
    "Посчитаем общий CR за весь период "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "803d4354",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>adv</th>\n",
       "      <th colspan=\"2\" halign=\"left\">target</th>\n",
       "      <th>CR</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>count</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>46288</td>\n",
       "      <td>1475910</td>\n",
       "      <td>3.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4021</td>\n",
       "      <td>256280</td>\n",
       "      <td>1.57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   adv target             CR\n",
       "          sum    count      \n",
       "0  0.0  46288  1475910  3.14\n",
       "1  1.0   4021   256280  1.57"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calc31 = sessions_df.groupby(['adv'], as_index=False).agg({'target':['sum', 'count']})\n",
    "calc31['CR'] = round((calc31['target',   'sum'] / calc31['target', 'count'] * 100),2)\n",
    "adv_vol = calc31.sort_values(by=[('target',   'count')],ascending=False)\n",
    "adv_vol"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd9df809",
   "metadata": {},
   "source": [
    "Посмотрим распределение по месяцам"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "4f19f767",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>adv</th>\n",
       "      <th>month</th>\n",
       "      <th colspan=\"2\" halign=\"left\">target</th>\n",
       "      <th>CR</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>count</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6427</td>\n",
       "      <td>104575</td>\n",
       "      <td>6.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>6.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6291</td>\n",
       "      <td>143165</td>\n",
       "      <td>4.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>318</td>\n",
       "      <td>17457</td>\n",
       "      <td>1.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4818</td>\n",
       "      <td>166548</td>\n",
       "      <td>2.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>69</td>\n",
       "      <td>8160</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5030</td>\n",
       "      <td>125451</td>\n",
       "      <td>4.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>468</td>\n",
       "      <td>29552</td>\n",
       "      <td>1.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5347</td>\n",
       "      <td>188014</td>\n",
       "      <td>2.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>688</td>\n",
       "      <td>55789</td>\n",
       "      <td>1.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6447</td>\n",
       "      <td>216137</td>\n",
       "      <td>2.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>667</td>\n",
       "      <td>47317</td>\n",
       "      <td>1.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5655</td>\n",
       "      <td>220476</td>\n",
       "      <td>2.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>786</td>\n",
       "      <td>43320</td>\n",
       "      <td>1.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>6273</td>\n",
       "      <td>311544</td>\n",
       "      <td>2.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1024</td>\n",
       "      <td>54669</td>\n",
       "      <td>1.87</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    adv month target            CR\n",
       "                 sum   count      \n",
       "0   0.0   5.0   6427  104575  6.15\n",
       "8   1.0   5.0      1      16  6.25\n",
       "1   0.0   6.0   6291  143165  4.39\n",
       "9   1.0   6.0    318   17457  1.82\n",
       "2   0.0   7.0   4818  166548  2.89\n",
       "10  1.0   7.0     69    8160  0.85\n",
       "3   0.0   8.0   5030  125451  4.01\n",
       "11  1.0   8.0    468   29552  1.58\n",
       "4   0.0   9.0   5347  188014  2.84\n",
       "12  1.0   9.0    688   55789  1.23\n",
       "5   0.0  10.0   6447  216137  2.98\n",
       "13  1.0  10.0    667   47317  1.41\n",
       "6   0.0  11.0   5655  220476  2.56\n",
       "14  1.0  11.0    786   43320  1.81\n",
       "7   0.0  12.0   6273  311544  2.01\n",
       "15  1.0  12.0   1024   54669  1.87"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calc32 = sessions_df.groupby(['adv', 'month'], as_index=False).agg({'target':['sum', 'count']})\n",
    "calc32['CR'] = round((calc32['target',   'sum'] / calc32['target', 'count'] * 100),2)\n",
    "adv_month = calc32.sort_values(by=['month'])\n",
    "adv_month"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea06affa",
   "metadata": {},
   "source": [
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